Enrichissement en histone des motifs Svb fixé ou non

1 Les motifs de Svb sont compris dans les fenêtre de 6kb (± 3kb)

1.1 Determination des motifs ayant de la fixation ou non

La recherche des motifs présentant de la fixation de Shavenbaby se fait en comparant les motifss ovoQ6 et svbF7 ( en les augmentant de 3kb ( start - 3kb / end + 3kb )) et les peaks Svb ( avec un peak-calling avec des sensibilités différentes ( q= 10-1 (stringent) ; q=10-4 (sensibily or low)) )

load("Myworkspace.RData")
svbF7_cons = read.table("/Users/alexmanchenoferris/Documents/motif_svb/svbF7_cons_flanked_dm6.bed")
ovoQ6_droso = read.table("/Users/alexmanchenoferris/Documents/motif_svb/ovoQ6_droso_flanked_dm6.bed")

svbF7_cons_gr = GRanges(seqnames = Rle(svbF7_cons$chr), 
                     ranges=IRanges(start = svbF7_cons$start,end = svbF7_cons$end, names = paste(rep("svbF7_cons",nrow(svbF7_cons)),rownames(svbF7_cons), sep = "_")))

ovoQ6_droso_gr_unique = unique(GRanges(seqnames = Rle(ovoQ6_droso$chr), 
                     ranges=IRanges(start = ovoQ6_droso$start,end = ovoQ6_droso$end, names = paste(rep("ovoQ6_droso",nrow(unique(ovoQ6_droso))),rownames(ovoQ6_droso), sep = "_"))))






SvbACT_peaks_low = read.table("/Users/alexmanchenoferris/Documents/Chipseq_ActRep/macs/low_quality/macs_1B_peaks.xls",sep = "\t", comment.char = "#", header = T, stringsAsFactors = F)

SvbACT.peaks_low = GRanges(seqnames = Rle(SvbACT_peaks_low[,1]), 
                     ranges=IRanges(start = SvbACT_peaks_low[,2],end = SvbACT_peaks_low[,3], names = SvbACT_peaks_low[,10]),
                     strand = Rle(rep("*",nrow(SvbACT_peaks_low))),
                     score = SvbACT_peaks_low[,7],
                   fold_enrichment = SvbACT_peaks_low[,8])



SvbACT_peaks_embryo = read.table("/Users/alexmanchenoferris/Documents/Chipseq_ActRep/macs/embryo/macs_svb_01_peaks.xls",sep = "\t", comment.char = "#", header = T, stringsAsFactors = F)



SvbACT.peaks_embryo = GRanges(seqnames = Rle(SvbACT_peaks_embryo[,1]), 
                     ranges=IRanges(start = SvbACT_peaks_embryo[,2],end = SvbACT_peaks_embryo[,3], names = SvbACT_peaks_embryo[,10]),
                     strand = Rle(rep("*",nrow(SvbACT_peaks_embryo))),
                     score = SvbACT_peaks_embryo[,7],
                   fold_enrichment = SvbACT_peaks_embryo[,8])

SvbACT_peaks_associated = read.table("/Users/alexmanchenoferris/Documents/ListeGene/peak_associed_geneDE/coordonnees_peak_associed_gene_diff_0-5kb.bed",sep = "\t", comment.char = "#", header = T, stringsAsFactors = F)



SvbACT.peaks_assiociated = GRanges(seqnames = Rle(SvbACT_peaks_associated[,1]), 
                     ranges=IRanges(start = SvbACT_peaks_associated[,2],end = SvbACT_peaks_associated[,3], names = SvbACT_peaks_associated$names),
                     strand = Rle(rep("*",nrow(SvbACT_peaks_associated))),
                     score = SvbACT_peaks_associated$scores,
                   fold_enrichment = SvbACT_peaks_associated$fold)

promoters_directgenes = read.table("/Users/alexmanchenoferris/Documents/ListeGene/direct_target_gene_coordonnees/promoters_gene_0_5b_tab.bed")
colnames(promoters_directgenes) = c("chr","start","end","length","strand")
promoters_directgenes_gr = GRanges(seqnames = Rle(promoters_directgenes$chr), 
                     ranges=IRanges(start = promoters_directgenes$start,end = promoters_directgenes$end, names = paste(rep("promoters",nrow(promoters_directgenes)),rownames(promoters_directgenes), sep = "_")))


# en flankant les motifs de 300kb ( taille moyenne des pics )
svbF7_cons_300 = read.table("/Users/alexmanchenoferris/Documents/motif_svb/300pb/svbF7_cons.bed")
ovoQ6_droso_300 = read.table("/Users/alexmanchenoferris/Documents/motif_svb/300pb/ovoQ6.bed")
colnames(ovoQ6_droso_300) = c("chr","start","end")
colnames(svbF7_cons_300) = c("chr","start","end")

svbF7_cons_gr_300 = GRanges(seqnames = Rle(svbF7_cons_300$chr), 
                     ranges=IRanges(start = svbF7_cons_300$start,end = svbF7_cons_300$end, names = paste(rep("svbF7_cons",nrow(svbF7_cons_300)),rownames(svbF7_cons_300), sep = "_")))

ovoQ6_droso_gr_unique_300 = unique(GRanges(seqnames = Rle(ovoQ6_droso_300$chr), 
                     ranges=IRanges(start = ovoQ6_droso_300$start,end = ovoQ6_droso_300$end, names = paste(rep("ovoQ6_droso",nrow(unique(ovoQ6_droso_300))),rownames(ovoQ6_droso_300), sep = "_"))))

1.1.1 Stringent

## $p.value
##      SvbAct ovoQ6 svbF7 pval
## [1,]      0     1     1  NaN
## [2,]      1     0     1  NaN
## [3,]      1     1     0  NaN
## 
## $vennCounts
##      SvbAct ovoQ6 svbF7 Counts count.B1.peaks count.ovoQ6_droso_gr_unique
## [1,]      0     0     0 -45933              0                           0
## [2,]      0     0     1    182              0                           0
## [3,]      0     1     0  39903              0                       39903
## [4,]      0     1     1   1223              0                        8039
## [5,]      1     0     0    829            829                           0
## [6,]      1     0     1     63             94                           0
## [7,]      1     1     0   3076           3103                       10374
## [8,]      1     1     1    757           1486                        7207
##      count.svbF7_cons_gr
## [1,]                   0
## [2,]                 182
## [3,]                   0
## [4,]                1223
## [5,]                   0
## [6,]                  69
## [7,]                   0
## [8,]                 880
## attr(,"class")
## [1] "VennCounts"

## $p.value
##      SvbAct ovoQ6 pval
## [1,]      1     1  NaN
## 
## $vennCounts
##      SvbAct ovoQ6 Counts count.B1.peaks count.ovoQ6_droso_gr_unique
## [1,]      0     0 -55740              0                           0
## [2,]      0     1  50359              0                       50359
## [3,]      1     0    925            925                           0
## [4,]      1     1   4556           4587                       15164
## attr(,"class")
## [1] "VennCounts"

## $p.value
##      SvbAct svbF7 pval
## [1,]      1     1  NaN
## 
## $vennCounts
##      SvbAct svbF7 Counts count.B1.peaks count.svbF7_cons_gr
## [1,]      0     0  -6859              0                   0
## [2,]      0     1   1751              0                1751
## [3,]      1     0   4652           4652                   0
## [4,]      1     1    556            860                 603
## attr(,"class")
## [1] "VennCounts"

svbF7_motifs_unbound = as.data.frame(svbF7_cons_gr_svb_bind_overlap$peaklist$svbF7_cons_gr)

svbF7_motifs_bound = as.data.frame(svbF7_cons_gr_svb_bind_overlap$peaklist$`B1.peaks///svbF7_cons_gr`)

ovoQ6_motifs_unbound = as.data.frame(ovoQ6_droso_svb_bind_overlap$peaklist$ovoQ6_droso_gr_unique)

ovoQ6_motifs_bound = as.data.frame(ovoQ6_droso_svb_bind_overlap$peaklist$`B1.peaks///ovoQ6_droso_gr_unique`)

1.1.2 Sensible

## $p.value
##      SvbAct ovoQ6.motif svbF7 pval
## [1,]      0           1     1  NaN
## [2,]      1           0     1  NaN
## [3,]      1           1     0  NaN
## 
## $vennCounts
##      SvbAct ovoQ6.motif svbF7 Counts count.SvbACT.peaks_low
## [1,]      0           0     0 -44598                      0
## [2,]      0           0     1    175                      0
## [3,]      0           1     0  37562                      0
## [4,]      0           1     1   1119                      0
## [5,]      1           0     0   1009                   1009
## [6,]      1           0     1     70                    111
## [7,]      1           1     0   3907                   3946
## [8,]      1           1     1    856                   1872
##      count.ovoQ6_droso_gr_unique count.svbF7_cons_gr
## [1,]                           0                   0
## [2,]                           0                 175
## [3,]                       37562                   0
## [4,]                        7263                1119
## [5,]                           0                   0
## [6,]                           0                  76
## [7,]                       12383                   0
## [8,]                        8315                 984
## attr(,"class")
## [1] "VennCounts"

## $p.value
##      SvbAct ovoQ6 pval
## [1,]      1     1  NaN
## 
## $vennCounts
##      SvbAct ovoQ6 Counts count.SvbACT.peaks_low count.ovoQ6_droso_gr_unique
## [1,]      0     0 -54247                      0                           0
## [2,]      0     1  47459                      0                       47459
## [3,]      1     0   1121                   1121                           0
## [4,]      1     1   5767                   5817                       18064
## attr(,"class")
## [1] "VennCounts"

## $p.value
##      SvbAct svbF7 pval
## [1,]      1     1  NaN
## 
## $vennCounts
##      SvbAct svbF7 Counts count.SvbACT.peaks_low count.svbF7_cons_gr
## [1,]      0     0  -8112                      0                   0
## [2,]      0     1   1671                      0                1671
## [3,]      1     0   5912                   5912                   0
## [4,]      1     1    629                   1026                 683
## attr(,"class")
## [1] "VennCounts"

1.1.3 Dans les embryons

## $p.value
##      SvbAct ovoQ6 svbF7 pval
## [1,]      0     1     1  NaN
## [2,]      1     0     1  NaN
## [3,]      1     1     0  NaN
## 
## $vennCounts
##      SvbAct ovoQ6 svbF7 Counts count.SvbACT.peaks_embryo
## [1,]      0     0     0 -42353                         0
## [2,]      0     0     1    151                         0
## [3,]      0     1     0  34214                         0
## [4,]      0     1     1    672                         0
## [5,]      1     0     0   1228                      1228
## [6,]      1     0     1     95                       133
## [7,]      1     1     0   4855                      4915
## [8,]      1     1     1   1238                      2430
##      count.ovoQ6_droso_gr_unique count.svbF7_cons_gr
## [1,]                           0                   0
## [2,]                           0                 151
## [3,]                       34214                   0
## [4,]                        4420                 672
## [5,]                           0                   0
## [6,]                           0                 100
## [7,]                       15239                   0
## [8,]                       11650                1431
## attr(,"class")
## [1] "VennCounts"

## $p.value
##      SvbAct ovoQ6 pval
## [1,]      1     1  NaN
## 
## $vennCounts
##      SvbAct ovoQ6 Counts count.SvbACT.peaks_embryo count.ovoQ6_droso_gr_unique
## [1,]      0     0 -50909                         0                           0
## [2,]      0     1  42375                         0                       42375
## [3,]      1     0   1365                      1365                           0
## [4,]      1     1   7269                      7341                       23148
## attr(,"class")
## [1] "VennCounts"

## $p.value
##      SvbAct svbF7 pval
## [1,]      1     1  NaN
## 
## $vennCounts
##      SvbAct svbF7 Counts count.SvbACT.peaks_embryo count.svbF7_cons_gr
## [1,]      0     0  -9557                         0                   0
## [2,]      0     1   1359                         0                1359
## [3,]      1     0   7379                      7379                   0
## [4,]      1     1    919                      1327                 995
## attr(,"class")
## [1] "VennCounts"

1.1.4 Dans les peaks associés aux gènes cibles

## $p.value
##      SvbAct ovoQ6 svbF7 pval
## [1,]      0     1     1  NaN
## [2,]      1     0     1  NaN
## [3,]      1     1     0  NaN
## 
## $vennCounts
##      SvbAct ovoQ6 svbF7 Counts count.unique.SvbACT.peaks_assiociated.
## [1,]      0     0     0 -52876                                      0
## [2,]      0     0     1    245                                      0
## [3,]      0     1     0  50359                                      0
## [4,]      0     1     1   2012                                      0
## [5,]      1     0     0     67                                     67
## [6,]      1     0     1      5                                      5
## [7,]      1     1     0    220                                    220
## [8,]      1     1     1     68                                     93
##      count.ovoQ6_droso_gr_unique count.svbF7_cons_gr
## [1,]                           0                   0
## [2,]                           0                 245
## [3,]                       50359                   0
## [4,]                       13531                2012
## [5,]                           0                   0
## [6,]                           0                   6
## [7,]                         906                   0
## [8,]                         727                  91
## attr(,"class")
## [1] "VennCounts"

## $p.value
##      SvbAct ovoQ6 pval
## [1,]      1     1  NaN
## 
## $vennCounts
##      SvbAct ovoQ6 Counts count.unique.SvbACT.peaks_assiociated.
## [1,]      0     0 -64497                                      0
## [2,]      0     1  64212                                      0
## [3,]      1     0     72                                     72
## [4,]      1     1    313                                    313
##      count.ovoQ6_droso_gr_unique
## [1,]                           0
## [2,]                       64212
## [3,]                           0
## [4,]                        1311
## attr(,"class")
## [1] "VennCounts"

## $p.value
##      SvbAct svbF7 pval
## [1,]      1     1  NaN
## 
## $vennCounts
##      SvbAct svbF7 Counts count.unique.SvbACT.peaks_assiociated.
## [1,]      0     0  -2574                                      0
## [2,]      0     1   2298                                      0
## [3,]      1     0    328                                    328
## [4,]      1     1     48                                     57
##      count.svbF7_cons_gr
## [1,]                   0
## [2,]                2298
## [3,]                   0
## [4,]                  56
## attr(,"class")
## [1] "VennCounts"

1.1.5 Dans les promotesurs des gènes cibles directes de Svb

## $p.value
##      SvbAct ovoQ6 svbF7 pval
## [1,]      0     1     1  NaN
## [2,]      1     0     1  NaN
## [3,]      1     1     0  NaN
## 
## $vennCounts
##      SvbAct ovoQ6 svbF7 Counts count.unique.promoters_directgenes_gr.
## [1,]      0     0     0 -52748                                      0
## [2,]      0     0     1    249                                      0
## [3,]      0     1     0  50336                                      0
## [4,]      0     1     1   2021                                      0
## [5,]      1     0     0     31                                     31
## [6,]      1     0     1      2                                      2
## [7,]      1     1     0    158                                    158
## [8,]      1     1     1     51                                     56
##      count.ovoQ6_droso_gr_unique count.svbF7_cons_gr
## [1,]                           0                   0
## [2,]                           0                 249
## [3,]                       50336                   0
## [4,]                       13587                2021
## [5,]                           0                   0
## [6,]                           0                   2
## [7,]                         921                   0
## [8,]                         679                  82
## attr(,"class")
## [1] "VennCounts"

## $p.value
##      SvbAct ovoQ6 pval
## [1,]      1     1  NaN
## 
## $vennCounts
##      SvbAct ovoQ6 Counts count.unique.promoters_directgenes_gr.
## [1,]      0     0 -64383                                      0
## [2,]      0     1  64236                                      0
## [3,]      1     0     33                                     33
## [4,]      1     1    214                                    214
##      count.ovoQ6_droso_gr_unique
## [1,]                           0
## [2,]                       64236
## [3,]                           0
## [4,]                        1287
## attr(,"class")
## [1] "VennCounts"

## $p.value
##      SvbAct svbF7 pval
## [1,]      1     1  NaN
## 
## $vennCounts
##      SvbAct svbF7 Counts count.unique.promoters_directgenes_gr.
## [1,]      0     0  -2463                                      0
## [2,]      0     1   2317                                      0
## [3,]      1     0    216                                    216
## [4,]      1     1     30                                     31
##      count.svbF7_cons_gr
## [1,]                   0
## [2,]                2317
## [3,]                   0
## [4,]                  37
## attr(,"class")
## [1] "VennCounts"

1.2 Enrichissement en histone des motifs SvbF7 fixé ou non

J’utilise la fonction de bedtools hypergéométrique avec comme option " a : les SvbF7 motif b : les marque d’histones et l’overlap est de 1bp

1.2.1 Stringent

1.2.1.1 H3K4me1

	in H3K4me1	absent H3K4me1
in svbF7 unbound	578	811
absent svbF7 unbound	8188	5736

	in H3K4me1	absent H3K4me1
in svbF7 bound	633	0
absent svbF7 bound	8139	6732

pvalue_fixe = phyper(633 - 1, 486, 15504 - 486, 8772)
pvalue_nonfixe = phyper(578 - 1, 1389, 15313 - 1389, 8766)
presence = c(633,8139)
absence = c(0,6732)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me1_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me1_fixe)

presence = c(578,8188)
absence = c(811,5736)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me1_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me1_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 4.409109110^{-35}

La pvalue du test de fisher pour les motifs fixé est de 1.024422510^{-161} Celle du test de fisher pour les motifs non fixé est de 1.413975810^{-34}`

total_svbF7_unbound = 578 + 811
total_svbF7_bound = 633

percent_svbf7_H3K4me1_unbound = round(100 * 578 / total_svbF7_unbound, 2 ) 
percent_svbf7_unbound_without_H3K4me1 = round(100 * 811 / total_svbF7_unbound, 2 ) 

percent_svbf7_H3K4me1_bound = round(100 * 633 / total_svbF7_bound, 2 ) 
percent_svbf7_bound_without_H3K4me1 = round(100 * 0 / total_svbF7_bound, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me1_absent = c(percent_svbf7_bound_without_H3K4me1,percent_svbf7_unbound_without_H3K4me1)

H3K4me1_present = c(percent_svbf7_H3K4me1_bound,percent_svbf7_H3K4me1_unbound)

data_H3K4me1 = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1,rownames = F,filter = "none",colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig3 = plot_ly(data_H3K4me1, x = ~etat_svb,y = ~H3K4me1_absent, type = "bar",name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_present, name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig3

1.2.1.2 H3K4me3

	in H3K4me3	absent H3K4me3
in svbF7 unbound	149	1240
absent svbF7 unbound	4658	10496

	in H3K4me3	absent H3K4me3
in svbF7 bound	359	127
absent svbF7 bound	4449	11833

pvalue_fixe = phyper(633 - 1, 486, 15504 - 486, 8772)
pvalue_nonfixe = phyper(149 - 1, 1389, 16543 - 1389, 4807)
presence = c(359,4449)
absence = c(127,11833)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me3_fixe)

presence = c(149,4658)
absence = c(1240,10496)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 2.377300810^{-66}

La pvalue du test de fisher pour les motifs fixé est de 2.264480710^{-97} Celle du test de fisher pour les motifs non fixé est de 1.30972510^{-65}`

total_svbF7_unbound_H3K4me3 = 149 + 1240
total_svbF7_bound_H3K4me3 = 359 + 127

percent_svbf7_H3K4me3_unbound = round(100 * 149 / total_svbF7_unbound_H3K4me3, 2 ) 
percent_svbf7_unbound_without_H3K4me3 = round(100 * 1240 / total_svbF7_unbound_H3K4me3, 2 ) 

percent_svbf7_H3K4me3_bound = round(100 * 359 / total_svbF7_bound_H3K4me3, 2 ) 
percent_svbf7_bound_without_H3K4me3 = round(100 * 127 / total_svbF7_bound_H3K4me3, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me3_absent = c(percent_svbf7_bound_without_H3K4me3,percent_svbf7_unbound_without_H3K4me3)

H3K4me3_present = c(percent_svbf7_H3K4me3_bound,percent_svbf7_H3K4me3_unbound)

data_H3K4me3 = data.frame(etat_svb,H3K4me3_absent,H3K4me3_present)
datatable(data_H3K4me3,rownames = F,filter = "none",colnames = c("Regions without H3K4me3","Regions with H3K4me3" ))

fig4 = plot_ly(data_H3K4me3, x = ~etat_svb,y = ~H3K4me3_absent, type = "bar",name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_present, name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig4

1.2.1.2.1 Rapport K4me1/ K4me3

total_K4me1_S2 = 8782
total_K4me3_S2 = 4812
rapp_fixe = round((contingence_H3K4me1_fixe[1,1] / total_K4me1_S2 ) / (contingence_H3K4me3_fixe[1,1] / total_K4me3_S2),2)
rapp_nonfixe = round((contingence_H3K4me1_nonfixe[1,1] / total_K4me1_S2)  / (contingence_H3K4me3_nonfixe[1,1] / total_K4me3_S2) ,2)

Il y a 0.97 fois de H3K4me1 que de H3K4me3 fixé.

Il y a 2.13 fois de H3K4me1 que de H3K4me3 non fixé.

1.2.1.3 H3K27ac

	in H3K27ac	absent H3K27ac
in svbF7 unbound	286	1103
absent svbF7 unbound	4862	7327

	in H3K27ac	absent H3K27ac
in svbF7 bound	435	51
absent svbF7 bound	4718	8527

pvalue_fixe = phyper(435 - 1, 486, 13731 - 486, 5153)
pvalue_nonfixe = phyper(286 - 1, 1389, 13578 - 1389, 5148)
presence = c(435,4718)
absence = c(51,8527)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27ac_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27ac_fixe)

presence = c(286,4862)
absence = c(1103,7327)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27ac_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 6.787045710^{-49}

La pvalue du test de fisher pour les motifs fixé est de 4.16793910^{-131} Celle du test de fisher pour les motifs non fixé est de 2.604877810^{-48}`

total_svbF7_unbound_H3K27ac = 286 + 1103
total_svbF7_bound_H3K27ac = 435 + 51

percent_svbf7_H3K27ac_unbound = round(100 * 286 / total_svbF7_unbound_H3K27ac, 2 ) 
percent_svbf7_unbound_without_H3K27ac = round(100 * 1103 / total_svbF7_unbound_H3K27ac, 2 ) 

percent_svbf7_H3K27ac_bound = round(100 * 435 / total_svbF7_bound_H3K27ac, 2 ) 
percent_svbf7_bound_without_H3K27ac = round(100 * 51 / total_svbF7_bound_H3K27ac, 2 ) 


etat_svb = c("svbF7_bound","svb_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)

H3K27ac_present = c(percent_svbf7_H3K27ac_bound,percent_svbf7_H3K27ac_unbound)

data_H3K27ac = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

fig1 = plot_ly(data_H3K27ac, x = ~etat_svb,y = ~H3K27ac_absent, type = "bar",name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_present, name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb")
fig1

fig1 = plot_ly(data_H3K27ac, x = ~etat_svb,y = ~H3K27ac_absent, type = "bar",name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_present, name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig1

1.2.1.4 H3K27me3

	in H3K27me3	absent H3K27me3
in svbF7 unbound	1868	0
absent svbF7 unbound	11567	4071

	in H3K27me3	absent H3K27me3
in svbF7 bound	311	175
absent svbF7 bound	13127	4145

pvalue_fixe = phyper(311 - 1, 486, 17758 - 486, 13438)
pvalue_nonfixe = phyper(286 - 1, 1389, 13578 - 1389, 5148)
presence = c(311,13127)
absence = c(175,4145)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27me3_fixe)

presence = c(1868,11567)
absence = c(0,4071)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1.97262510^{-9} Celle du test de hypergéométrique pour les motifs non fixé est de 6.787045710^{-49}

La pvalue du test de fisher pour les motifs fixé est de 5.596902110^{-9} Celle du test de fisher pour les motifs non fixé est de 2.022617710^{-229}`

total_svbF7_unbound_H3K27me3 = 1868 + 0
total_svbF7_bound_H3K27me3 = 311 + 175

percent_svbf7_H3K27me3_unbound = round(100 * 1868 / total_svbF7_unbound_H3K27me3, 2 ) 
percent_svbf7_unbound_without_H3K27me3 = round(100 * 0 / total_svbF7_unbound_H3K27me3, 2 ) 

percent_svbf7_H3K27me3_bound = round(100 * 311 / total_svbF7_bound_H3K27me3, 2 ) 
percent_svbf7_bound_without_H3K27me3 = round(100 * 175 / total_svbF7_bound_H3K27me3, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K27me3_absent = c(percent_svbf7_bound_without_H3K27me3,percent_svbf7_unbound_without_H3K27me3)

H3K27me3_present = c(percent_svbf7_H3K27me3_bound,percent_svbf7_H3K27me3_unbound)

data_H3K27me3 = data.frame(etat_svb,H3K27me3_absent,H3K27me3_present)
datatable(data_H3K27me3,rownames = F,filter = "none",colnames = c("Regions without H3K27me3","Regions with H3K27me3" ))

fig2 = plot_ly(data_H3K27me3, x = ~etat_svb,y = ~H3K27me3_absent, type = "bar",name = "H3K27me3 absent", color = I("grey"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_present, name = "H3K27me3 present", color = I("purple"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig2

1.2.2 Sensible

1.2.2.1 H3K4me1

	in H3K4me1	absent H3K4me1
in svbF7 unbound	514	813
absent svbF7 unbound	8252	5729

	in H3K4me1	absent H3K4me1
in svbF7 bound	698	0
absent svbF7 bound	8074	6722

pvalue_fixe = phyper(698 - 1, 549, 15494 - 549, 8772)
pvalue_nonfixe = phyper(514 - 1, 1327, 15308 - 1327, 8766)
presence = c(698,8074)
absence = c(0,6722)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me1_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me1_fixe)

presence = c(514,8252)
absence = c(813,5729)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me1_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me1_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 3.231743410^{-46}

La pvalue du test de fisher pour les motifs fixé est de 1.39957510^{-178} Celle du test de fisher pour les motifs non fixé est de 1.266940710^{-45}`

total_svbF7_unbound = 514 + 813
total_svbF7_bound = 698

percent_svbf7_H3K4me1_unbound = round(100 * 514 / total_svbF7_unbound, 2 ) 
percent_svbf7_unbound_without_H3K4me1 = round(100 * 813 / total_svbF7_unbound, 2 ) 

percent_svbf7_H3K4me1_bound = round(100 * 698 / total_svbF7_bound, 2 ) 
percent_svbf7_bound_without_H3K4me1 = round(100 * 0 / total_svbF7_bound, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me1_absent = c(percent_svbf7_bound_without_H3K4me1,percent_svbf7_unbound_without_H3K4me1)

H3K4me1_present = c(percent_svbf7_H3K4me1_bound,percent_svbf7_H3K4me1_unbound)

data_H3K4me1 = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1,rownames = F,filter = "none", colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig3 = plot_ly(data_H3K4me1, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig3

data_H3K4me1 = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1,rownames = F,filter = "none", colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig3 = plot_ly(data_H3K4me1, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb")
fig3

1.2.2.2 H3K4me3

	in H3K4me3	absent H3K4me3
in svbF7 unbound	121	1206
absent svbF7 unbound	4686	10524

	in H3K4me3	absent H3K4me3
in svbF7 bound	388	161
absent svbF7 bound	4420	11787

pvalue_fixe = phyper(388 - 1, 549, 16756 - 549, 4808)
pvalue_nonfixe = phyper(121 - 1, 1327, 16537 - 1327, 4807)
presence = c(388,4420)
absence = c(161,11787)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me3_fixe)

presence = c(121,4686)
absence = c(1206,10524)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 2.348151110^{-76}

La pvalue du test de fisher pour les motifs fixé est de 2.414317810^{-95} Celle du test de fisher pour les motifs non fixé est de 1.779050610^{-75}`

total_svbF7_unbound_H3K4me3 = 121 + 1206
total_svbF7_bound_H3K4me3 = 388 + 161

percent_svbf7_H3K4me3_unbound = round(100 * 121 / total_svbF7_unbound_H3K4me3, 2 ) 
percent_svbf7_unbound_without_H3K4me3 = round(100 * 1206 / total_svbF7_unbound_H3K4me3, 2 ) 

percent_svbf7_H3K4me3_bound = round(100 * 388 / total_svbF7_bound_H3K4me3, 2 ) 
percent_svbf7_bound_without_H3K4me3 = round(100 * 161 / total_svbF7_bound_H3K4me3, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me3_absent = c(percent_svbf7_bound_without_H3K4me3,percent_svbf7_unbound_without_H3K4me3)

H3K4me3_present = c(percent_svbf7_H3K4me3_bound,percent_svbf7_H3K4me3_unbound)

data_H3K4me3 = data.frame(etat_svb,H3K4me3_absent,H3K4me3_present)
datatable(data_H3K4me3,rownames = F,filter = "none",colnames = c("Regions without H3K4me3","Regions with H3K4me3" ))

fig4 = plot_ly(data_H3K4me3, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig4

fig4 = plot_ly(data_H3K4me3, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb")
fig4

1.2.2.2.1 Rapport K4me1/ K4me3

total_K4me1_S2 = 8782
total_K4me3_S2 = 4812
rapp_fixe = round((contingence_H3K4me1_fixe[1,1] / total_K4me1_S2 ) / (contingence_H3K4me3_fixe[1,1] / total_K4me3_S2),2)
rapp_nonfixe = round((contingence_H3K4me1_nonfixe[1,1] / total_K4me1_S2)  / (contingence_H3K4me3_nonfixe[1,1] / total_K4me3_S2) ,2)

Il y a 0.99 fois de H3K4me1 que de H3K4me3 fixé.

Il y a 2.33 fois de H3K4me1 que de H3K4me3 non fixé.

1.2.2.3 H3K27ac

	in H3K27ac	absent H3K27ac
in svbF7 unbound	244	1083
absent svbF7 unbound	4904	7343

	in H3K27ac	absent H3K27ac
in svbF7 bound	478	71
absent svbF7 bound	4675	8500

pvalue_fixe = phyper(478 - 1, 549, 13724 - 549, 5153)
pvalue_nonfixe = phyper(244 - 1, 1327, 13574 - 1327, 5148)
presence = c(478,4675)
absence = c(71,8500)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27ac_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27ac_fixe)

presence = c(244,4904)
absence = c(1083,7343)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27ac_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1.425593210^{-59}

La pvalue du test de fisher pour les motifs fixé est de 2.973567510^{-133} Celle du test de fisher pour les motifs non fixé est de 8.220511410^{-59}`

total_svbF7_unbound_H3K27ac = 244 + 1083
total_svbF7_bound_H3K27ac = 478 + 71

percent_svbf7_H3K27ac_unbound = round(100 * 244 / total_svbF7_unbound_H3K27ac, 2 ) 
percent_svbf7_unbound_without_H3K27ac = round(100 * 1083 / total_svbF7_unbound_H3K27ac, 2 ) 

percent_svbf7_H3K27ac_bound = round(100 * 478 / total_svbF7_bound_H3K27ac, 2 ) 
percent_svbf7_bound_without_H3K27ac = round(100 * 71 / total_svbF7_bound_H3K27ac, 2 ) 


etat_svb = c("svbF7_bound","svb_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)

H3K27ac_present = c(percent_svbf7_H3K27ac_bound,percent_svbf7_H3K27ac_unbound)

data_H3K27ac = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

# fig1 = plot_ly(data_H3K27ac, x = ~etat_svb,y = ~H3K27ac_absent, type = "bar",name = "H3K27ac absent")
# fig1 = fig1 %>% add_trace(y = ~H3K27ac_present, name = "H3K27ac present")
# fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
# fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb")
# fig1

fig1 = plot_ly(data_H3K27ac, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig1

fig1 = plot_ly(data_H3K27ac, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb")
fig1

1.2.2.4 H3K27me3

	in H3K27me3	absent H3K27me3
in svbF7 unbound	1791	0
absent svbF7 unbound	11644	4065

	in H3K27me3	absent H3K27me3
in svbF7 bound	392	71
absent svbF7 bound	13046	4151

pvalue_fixe = phyper(392 - 1, 549, 17746 - 549, 13438)
pvalue_nonfixe = phyper(1791 - 1, 1327, 17500 - 1327, 13435)
presence = c(392,13046)
absence = c(71,4151)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27me3_fixe)

presence = c(1791,11644)
absence = c(0,4065)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 0.0079173 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 5.493538610^{-6} Celle du test de fisher pour les motifs non fixé est de 4.53427810^{-219}`

total_svbF7_unbound_H3K27me3 = 1791 + 0
total_svbF7_bound_H3K27me3 = 392 + 157

percent_svbf7_H3K27me3_unbound = round(100 * 1791 / total_svbF7_unbound_H3K27me3, 2 ) 
percent_svbf7_unbound_without_H3K27me3 = round(100 * 0 / total_svbF7_unbound_H3K27me3, 2 ) 

percent_svbf7_H3K27me3_bound = round(100 * 392 / total_svbF7_bound_H3K27me3, 2 ) 
percent_svbf7_bound_without_H3K27me3 = round(100 * 157 / total_svbF7_bound_H3K27me3, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K27me3_absent = c(percent_svbf7_bound_without_H3K27me3,percent_svbf7_unbound_without_H3K27me3)

H3K27me3_present = c(percent_svbf7_H3K27me3_bound,percent_svbf7_H3K27me3_unbound)

data_H3K27me3 = data.frame(etat_svb,H3K27me3_absent,H3K27me3_present)
datatable(data_H3K27me3,rownames = F,filter = "none",colnames = c("Regions without H3K27me3","Regions with H3K27me3" ))

fig2 = plot_ly(data_H3K27me3, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig2

fig2 = plot_ly(data_H3K27me3, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb")
fig2

1.2.3 Histones en présence de Svb

Je regarde ici l’enrichisement en histones des motifs de Svb en présence des différentes formes de Svb ( Act/ Rep) et Ctrl

1.2.3.1 H3K4me1

1.2.3.1.1 Ctrl

	in H3K4me1	absent H3K4me1
in svbF7 unbound	818	571
absent svbF7 unbound	9368	4332

	in H3K4me1	absent H3K4me1
in svbF7 bound	698	0
absent svbF7 bound	9498	5082

pvalue_fixe = phyper(698 - 1, 486, 15278 - 486, 10196)
pvalue_nonfixe = phyper(818 - 1, 1389, 15089 - 1389, 10186)
presence = c(698,0)
absence = c(9498,5082)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me1_fixe_ctrl = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me1_fixe_ctrl)

presence = c(818,9368)
absence = c(571,4332)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me1_nonfixe_ctrl = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me1_nonfixe_ctrl)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 6.137004310^{-13}

La pvalue du test de fisher pour les motifs fixé est de 1.112238610^{-126} Celle du test de fisher pour les motifs non fixé est de 1.73553510^{-12}`

total_svbF7_unbound = 818 + 571
total_svbF7_bound = 698 + 0

percent_svbf7_H3K4me1_unbound = round(100 * 818 / total_svbF7_unbound, 2 ) 
percent_svbf7_unbound_without_H3K4me1 = round(100 * 571 / total_svbF7_unbound, 2 ) 

percent_svbf7_H3K4me1_bound = round(100 * 698 / total_svbF7_bound, 2 ) 
percent_svbf7_bound_without_H3K4me1 = round(100 * 0 / total_svbF7_bound, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me1_absent = c(percent_svbf7_bound_without_H3K4me1,percent_svbf7_unbound_without_H3K4me1)

H3K4me1_present = c(percent_svbf7_H3K4me1_bound,percent_svbf7_H3K4me1_unbound)

data_H3K4me1_ctrl = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1_ctrl,rownames = F,filter = "none", colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig3 = plot_ly(data_H3K4me1_ctrl, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Ctrl condition)", barmode = "stack")
fig3

fig3 = plot_ly(data_H3K4me1_ctrl, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Ctrl condition)")
fig3

1.2.3.1.2 Act

	in H3K4me1	absent H3K4me1
in svbF7 unbound	988	401
absent svbF7 unbound	11249	0

	in H3K4me1	absent H3K4me1
in svbF7 bound	753	0
absent svbF7 bound	11497	3863

pvalue_fixe = phyper(753 - 1, 486, 16113 - 486, 12250)
pvalue_nonfixe = phyper(988 - 1, 1389, 15904 - 1389, 12237)
presence = c(753,11497)
absence = c(0,3863)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me1_fixe_act = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me1_fixe_act)

presence = c(988,11249)
absence = c(0,401)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me1_nonfixe_act = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me1_nonfixe_act)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 6.005383710^{-8}

La pvalue du test de fisher pour les motifs fixé est de 1.450122910^{-92} Celle du test de fisher pour les motifs non fixé est de 6.41756610^{-15}`

total_svbF7_unbound = 988 + 401
total_svbF7_bound = 753 + 0

percent_svbf7_H3K4me1_unbound = round(100 * 988 / total_svbF7_unbound, 2 ) 
percent_svbf7_unbound_without_H3K4me1 = round(100 * 401 / total_svbF7_unbound, 2 ) 

percent_svbf7_H3K4me1_bound = round(100 * 753 / total_svbF7_bound, 2 ) 
percent_svbf7_bound_without_H3K4me1 = round(100 * 0 / total_svbF7_bound, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me1_absent = c(percent_svbf7_bound_without_H3K4me1,percent_svbf7_unbound_without_H3K4me1)

H3K4me1_present = c(percent_svbf7_H3K4me1_bound,percent_svbf7_H3K4me1_unbound)

data_H3K4me1_act = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1_act,rownames = F,filter = "none", colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig3 = plot_ly(data_H3K4me1_act, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Act condition)", barmode = "stack")
fig3

fig3 = plot_ly(data_H3K4me1_act, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Act condition)")
fig3

1.2.3.1.3 Rep

	in H3K4me1	absent H3K4me1
in svbF7 unbound	920	469
absent svbF7 unbound	10874	3453

	in H3K4me1	absent H3K4me1
in svbF7 bound	745	0
absent svbF7 bound	11057	4118

pvalue_fixe = phyper(745 - 1, 486, 15920 - 486, 11802)
pvalue_nonfixe = phyper(920 - 1, 1389, 17166 - 1389, 11794)
presence = c(745,11057)
absence = c(0,4118)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me1_fixe_rep = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me1_fixe_rep)

presence = c(920,10874)
absence = c(469,3453)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me1_nonfixe_rep = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me1_nonfixe_rep)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 0.0182405

La pvalue du test de fisher pour les motifs fixé est de 3.317013410^{-100} Celle du test de fisher pour les motifs non fixé est de 1.431330110^{-14}`

total_svbF7_unbound = 920 + 469
total_svbF7_bound = 745 + 0

percent_svbf7_H3K4me1_unbound = round(100 * 920 / total_svbF7_unbound, 2 ) 
percent_svbf7_unbound_without_H3K4me1 = round(100 * 469 / total_svbF7_unbound, 2 ) 

percent_svbf7_H3K4me1_bound = round(100 * 746 / total_svbF7_bound, 2 ) 
percent_svbf7_bound_without_H3K4me1 = round(100 * 0 / total_svbF7_bound, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me1_absent = c(percent_svbf7_bound_without_H3K4me1,percent_svbf7_unbound_without_H3K4me1)

H3K4me1_present = c(percent_svbf7_H3K4me1_bound,percent_svbf7_H3K4me1_unbound)

data_H3K4me1_rep = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1_rep,rownames = F,filter = "none", colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig3 = plot_ly(data_H3K4me1_rep, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Rep condition)", barmode = "stack")
fig3

fig3 = plot_ly(data_H3K4me1_rep, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Rep condition)")
fig3

1.2.3.2 H3K4me3

1.2.3.2.1 Ctrl

	in H3K4me3	absent H3K4me3
in svbF7 unbound	721	668
absent svbF7 unbound	9924	6743

	in H3K4me3	absent H3K4me3
in svbF7 bound	405	81
absent svbF7 bound	10240	7598

pvalue_fixe = phyper(405 - 1, 486, 18324 - 486, 10645)
pvalue_nonfixe = phyper(721 - 1, 1389, 18025 - 1389, 10645)
presence = c(405,81)
absence = c(10240,7598)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me3_fixe_ctrl = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me3_fixe_ctrl)

presence = c(721,9924)
absence = c(668,6743)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me3_nonfixe_ctrl = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me3_nonfixe_ctrl)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 8.857963910^{-9}

La pvalue du test de fisher pour les motifs fixé est de 2.206021510^{-33} Celle du test de fisher pour les motifs non fixé est de 3.492107710^{-8}`

total_svbF7_unbound_H3K4me3 = 721 + 668
total_svbF7_bound_H3K4me3 = 405 + 81

percent_svbf7_H3K4me3_unbound = round(100 * 721 / total_svbF7_unbound_H3K4me3, 2 ) 
percent_svbf7_unbound_without_H3K4me3 = round(100 * 668 / total_svbF7_unbound_H3K4me3, 2 ) 

percent_svbf7_H3K4me3_bound = round(100 * 405 / total_svbF7_bound_H3K4me3, 2 ) 
percent_svbf7_bound_without_H3K4me3 = round(100 * 81 / total_svbF7_bound_H3K4me3, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me3_absent = c(percent_svbf7_bound_without_H3K4me3,percent_svbf7_unbound_without_H3K4me3)

H3K4me3_present = c(percent_svbf7_H3K4me3_bound,percent_svbf7_H3K4me3_unbound)

data_H3K4me3_ctrl = data.frame(etat_svb,H3K4me3_absent,H3K4me3_present)
datatable(data_H3K4me3_ctrl,rownames = F,filter = "none",colnames = c("Regions without H3K4me3","Regions with H3K4me3" ))

fig4 = plot_ly(data_H3K4me3_ctrl, x = ~etat_svb,y = ~H3K4me3_absent, type = "bar",name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_present, name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb (Ctrl condition)", barmode = "stack")
fig4

	in H3K4me3	absent H3K4me3
in svbF7 unbound	343	1046
absent svbF7 unbound	6896	9899

	in H3K4me3	absent H3K4me3
in svbF7 bound	334	152
absent svbF7 bound	6906	11030

pvalue_fixe = phyper(334 - 1, 486, 18422 - 486, 7240)
pvalue_nonfixe = phyper(343 - 1, 1389, 18151 - 1389, 7239)
presence = c(334,6906)
absence = c(152,11030)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me3_fixe_act = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me3_fixe_act)

presence = c(343,6896)
absence = c(1046,9899)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me3_nonfixe_act = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me3_nonfixe_act)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1.091793410^{-35}

La pvalue du test de fisher pour les motifs fixé est de 4.034298210^{-40} Celle du test de fisher pour les motifs non fixé est de 8.501713810^{-35}`

total_svbF7_unbound_H3K4me3 = 343 + 1046
total_svbF7_bound_H3K4me3 = 334 + 152

percent_svbf7_H3K4me3_unbound = round(100 * 343 / total_svbF7_unbound_H3K4me3, 2 ) 
percent_svbf7_unbound_without_H3K4me3 = round(100 * 1046 / total_svbF7_unbound_H3K4me3, 2 ) 

percent_svbf7_H3K4me3_bound = round(100 * 334 / total_svbF7_bound_H3K4me3, 2 ) 
percent_svbf7_bound_without_H3K4me3 = round(100 * 152 / total_svbF7_bound_H3K4me3, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me3_absent = c(percent_svbf7_bound_without_H3K4me3,percent_svbf7_unbound_without_H3K4me3)

H3K4me3_present = c(percent_svbf7_H3K4me3_bound,percent_svbf7_H3K4me3_unbound)

data_H3K4me3_act = data.frame(etat_svb,H3K4me3_absent,H3K4me3_present)
datatable(data_H3K4me3_act,rownames = F,filter = "none",colnames = c("Regions without H3K4me3","Regions with H3K4me3" ))

fig4 = plot_ly(data_H3K4me3_act, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb (Act condition)", barmode = "stack")
fig4

fig4 = plot_ly(data_H3K4me3_act, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb (Act condition)")
fig4

1.2.3.2.2 Rep

	in H3K4me3	absent H3K4me3
in svbF7 unbound	146	1243
absent svbF7 unbound	4548	10940

	in H3K4me3	absent H3K4me3
in svbF7 bound	328	158
absent svbF7 bound	4467	12262

pvalue_fixe = phyper(328 - 1, 486, 17215 - 486, 4795)
pvalue_nonfixe = phyper(328 - 1, 1389, 18685 - 1389, 11861)
presence = c(328,4467)
absence = c(158,12262)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me3_fixe_rep = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me3_fixe_rep)

presence = c(328,4467)
absence = c(158,12262)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me3_nonfixe_rep = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me3_nonfixe_rep)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 5.097738610^{-219}

La pvalue du test de fisher pour les motifs fixé est de 7.836987810^{-76} Celle du test de fisher pour les motifs non fixé est de 7.836987810^{-76}`

total_svbF7_unbound_H3K4me3 = 146 + 1243
total_svbF7_bound_H3K4me3 = 328 + 158

percent_svbf7_H3K4me3_unbound = round(100 * 146 / total_svbF7_unbound_H3K4me3, 2 ) 
percent_svbf7_unbound_without_H3K4me3 = round(100 * 1243 / total_svbF7_unbound_H3K4me3, 2 ) 

percent_svbf7_H3K4me3_bound = round(100 * 328 / total_svbF7_bound_H3K4me3, 2 ) 
percent_svbf7_bound_without_H3K4me3 = round(100 * 158 / total_svbF7_bound_H3K4me3, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me3_absent = c(percent_svbf7_bound_without_H3K4me3,percent_svbf7_unbound_without_H3K4me3)

H3K4me3_present = c(percent_svbf7_H3K4me3_bound,percent_svbf7_H3K4me3_unbound)

data_H3K4me3_rep = data.frame(etat_svb,H3K4me3_absent,H3K4me3_present)
datatable(data_H3K4me3_rep,rownames = F,filter = "none",colnames = c("Regions without H3K4me3","Regions with H3K4me3" ))

fig4 = plot_ly(data_H3K4me3_rep, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb (Rep condition)", barmode = "stack")
fig4

fig4 = plot_ly(data_H3K4me3_rep, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb (Rep condition)")
fig4

1.2.3.2.3 Rapport K4me1/ K4me3

1.2.3.2.3.1 Ctrl

total_K4me1_S2 = 8782
total_K4me3_S2 = 4812
rapp_fixe = round((contingence_H3K4me1_fixe_ctrl[1,1] / total_K4me1_S2 ) / (contingence_H3K4me3_fixe_ctrl[1,1] / total_K4me3_S2),2)
rapp_nonfixe = round((contingence_H3K4me1_nonfixe_ctrl[1,1] / total_K4me1_S2)  / (contingence_H3K4me3_nonfixe_ctrl[1,1] / total_K4me3_S2) ,2)

Il y a 0.94 fois de H3K4me1 que de H3K4me3 fixé.

Il y a 0.62 fois de H3K4me1 que de H3K4me3 non fixé.

1.2.3.2.3.2 Act

total_K4me1_S2 = 8782
total_K4me3_S2 = 4812
rapp_fixe = round((contingence_H3K4me1_fixe_act [1,1] / total_K4me1_S2 ) / (contingence_H3K4me3_fixe_act[1,1] / total_K4me3_S2),2)
rapp_nonfixe = round((contingence_H3K4me1_nonfixe_act[1,1] / total_K4me1_S2)  / (contingence_H3K4me3_nonfixe_act[1,1] / total_K4me3_S2) ,2)

Il y a 1.24 fois de H3K4me1 que de H3K4me3 fixé.

Il y a 1.58 fois de H3K4me1 que de H3K4me3 non fixé.

1.2.3.2.3.3 Rep

total_K4me1_S2 = 8782
total_K4me3_S2 = 4812
rapp_fixe = round((contingence_H3K4me1_fixe_rep[1,1] / total_K4me1_S2 ) / (contingence_H3K4me3_fixe_rep[1,1] / total_K4me3_S2),2)
rapp_nonfixe = round((contingence_H3K4me1_nonfixe_rep[1,1] / total_K4me1_S2)  / (contingence_H3K4me3_nonfixe_rep[1,1] / total_K4me3_S2) ,2)

1.2.3.3 H3K27ac

1.2.3.3.1 Ctrl

	in H3K27ac	absent H3K27ac
in svbF7 unbound	213	1176
absent svbF7 unbound	5418	9019

	in H3K27ac	absent H3K27ac
in svbF7 bound	446	40
absent svbF7 bound	5189	10357

pvalue_fixe = phyper(446 - 1, 486, 16032 - 486, 5635)
pvalue_nonfixe = phyper(213 - 1, 1389, 15826 - 1389, 5631)
presence = c(446,5189)
absence = c(40,10357)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27ac_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27ac_fixe)

presence = c(213,5418)
absence = c(1176,9019)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27ac_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1.789608210^{-69}

La pvalue du test de fisher pour les motifs fixé est de 1.388775210^{-156} Celle du test de fisher pour les motifs non fixé est de 9.597761510^{-69}`

total_svbF7_unbound_H3K27ac = 213 + 1176
total_svbF7_bound_H3K27ac = 446 + 40

percent_svbf7_H3K27ac_unbound = round(100 * 213 / total_svbF7_unbound_H3K27ac, 2 ) 
percent_svbf7_unbound_without_H3K27ac = round(100 * 1176 / total_svbF7_unbound_H3K27ac, 2 ) 

percent_svbf7_H3K27ac_bound = round(100 * 446 / total_svbF7_bound_H3K27ac, 2 ) 
percent_svbf7_bound_without_H3K27ac = round(100 * 40 / total_svbF7_bound_H3K27ac, 2 ) 


etat_svb = c("svbF7_bound","svb_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)

H3K27ac_present = c(percent_svbf7_H3K27ac_bound,percent_svbf7_H3K27ac_unbound)

data_H3K27ac_ctrl = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac_ctrl,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

fig1 = plot_ly(data_H3K27ac_ctrl, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Ctrl condition)", barmode = "stack")
fig1

fig1 = plot_ly(data_H3K27ac_ctrl, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Ctrl condition)")
fig1

1.2.3.3.2 Act

	in H3K27ac	absent H3K27ac
in svbF7 unbound	206	1183
absent svbF7 unbound	5471	9125

	in H3K27ac	absent H3K27ac
in svbF7 bound	445	41
absent svbF7 bound	5236	10473

pvalue_fixe = phyper(445 - 1, 486, 166195 - 486, 5681)
pvalue_nonfixe = phyper(206 - 1, 1389, 15985 - 1389, 5677)
presence = c(445,5236)
absence = c(41,10473)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27ac_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27ac_fixe)

presence = c(206,5471)
absence = c(1183,9125)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27ac_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 9.47918510^{-73}

La pvalue du test de fisher pour les motifs fixé est de 1.405424510^{-155} Celle du test de fisher pour les motifs non fixé est de 4.607645810^{-72}`

total_svbF7_unbound_H3K27ac = 206 + 1183
total_svbF7_bound_H3K27ac = 445 + 41

percent_svbf7_H3K27ac_unbound = round(100 * 206 / total_svbF7_unbound_H3K27ac, 2 ) 
percent_svbf7_unbound_without_H3K27ac = round(100 * 1183 / total_svbF7_unbound_H3K27ac, 2 ) 

percent_svbf7_H3K27ac_bound = round(100 * 445 / total_svbF7_bound_H3K27ac, 2 ) 
percent_svbf7_bound_without_H3K27ac = round(100 * 41 / total_svbF7_bound_H3K27ac, 2 ) 


etat_svb = c("svbF7_bound","svb_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)

H3K27ac_present = c(percent_svbf7_H3K27ac_bound,percent_svbf7_H3K27ac_unbound)

data_H3K27ac_act = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac_act,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

fig1 = plot_ly(data_H3K27ac_act, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Act Condtion) ", barmode = "stack")
fig1

fig1 = plot_ly(data_H3K27ac_act, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Act Condtion) ")
fig1

1.2.3.3.3 Rep

	in H3K27ac	absent H3K27ac
in svbF7 unbound	206	1183
absent svbF7 unbound	5425	9033

	in H3K27ac	absent H3K27ac
in svbF7 bound	440	46
absent svbF7 bound	5294	10374

pvalue_fixe = phyper(440 - 1, 486, 16054 - 486, 5634)
pvalue_nonfixe = phyper(106 - 1, 1389, 15847 - 1389, 5631)
presence = c(440,5294)
absence = c(46,10374)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27ac_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27ac_fixe)

presence = c(206,5425)
absence = c(1183,9033)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27ac_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 3.024282210^{-142}

La pvalue du test de fisher pour les motifs fixé est de 7.595208310^{-147} Celle du test de fisher pour les motifs non fixé est de 3.794733510^{-72}`

total_svbF7_unbound_H3K27ac = 206 + 1183
total_svbF7_bound_H3K27ac = 440 + 46

percent_svbf7_H3K27ac_unbound = round(100 * 206 / total_svbF7_unbound_H3K27ac, 2 ) 
percent_svbf7_unbound_without_H3K27ac = round(100 * 1183 / total_svbF7_unbound_H3K27ac, 2 ) 

percent_svbf7_H3K27ac_bound = round(100 * 440 / total_svbF7_bound_H3K27ac, 2 ) 
percent_svbf7_bound_without_H3K27ac = round(100 * 46 / total_svbF7_bound_H3K27ac, 2 ) 


etat_svb = c("svbF7_bound","svb_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)

H3K27ac_present = c(percent_svbf7_H3K27ac_bound,percent_svbf7_H3K27ac_unbound)

data_H3K27ac_rep = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac_rep,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

fig1 = plot_ly(data_H3K27ac_rep, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Rep condition) ", barmode = "stack")
fig1

fig1 = plot_ly(data_H3K27ac_rep, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Rep condition) ")
fig1

1.2.3.4 H3K27me3

1.2.3.4.1 Ctrl

	in H3K27me3	absent H3K27me3
in svbF7 unbound	1928	0
absent svbF7 unbound	13199	0

	in H3K27me3	absent H3K27me3
in svbF7 bound	410	76
absent svbF7 bound	14717	87

pvalue_fixe = phyper(410 - 1, 486, 15290 - 486, 15127)
pvalue_nonfixe = phyper(1928 - 1, 1389, 15127 - 1389, 15127)
presence = c(410,14717)
absence = c(76,87)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27me3_fixe)

presence = c(1928,13199)
absence = c(0,0)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 5.489481710^{-72} Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 1.745591210^{-70} Celle du test de fisher pour les motifs non fixé est de 1`

total_svbF7_unbound_H3K27me3 = 1928 + 0
total_svbF7_bound_H3K27me3 = 410 + 76

percent_svbf7_H3K27me3_unbound = round(100 * 1928 / total_svbF7_unbound_H3K27me3, 2 ) 
percent_svbf7_unbound_without_H3K27me3 = round(100 * 0 / total_svbF7_unbound_H3K27me3, 2 ) 

percent_svbf7_H3K27me3_bound = round(100 * 410 / total_svbF7_bound_H3K27me3, 2 ) 
percent_svbf7_bound_without_H3K27me3 = round(100 * 76 / total_svbF7_bound_H3K27me3, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K27me3_absent = c(percent_svbf7_bound_without_H3K27me3,percent_svbf7_unbound_without_H3K27me3)

H3K27me3_present = c(percent_svbf7_H3K27me3_bound,percent_svbf7_H3K27me3_unbound)

data_H3K27me3_ctrl = data.frame(etat_svb,H3K27me3_absent,H3K27me3_present)
datatable(data_H3K27me3_ctrl,rownames = F,filter = "none",colnames = c("Regions without H3K27me3","Regions with H3K27me3" ))

fig2 = plot_ly(data_H3K27me3_ctrl, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb (Ctrl condition)", barmode = "stack")
fig2

fig2 = plot_ly(data_H3K27me3_ctrl, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb (Ctrl condition)")
fig2

1.2.3.4.2 Act

	in H3K27me3	absent H3K27me3
in svbF7 unbound	1863	0
absent svbF7 unbound	12446	119

	in H3K27me3	absent H3K27me3
in svbF7 bound	410	76
absent svbF7 bound	14717	87

pvalue_fixe = phyper(410 - 1, 486, 15290 - 486, 15127)
pvalue_nonfixe = phyper(1863 - 1, 1389, 14428 - 1389, 14309)
presence = c(410,14717)
absence = c(76,87)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27me3_fixe)

presence = c(1863,12446)
absence = c(0,119)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 5.489481710^{-72} Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 1.745591210^{-70} Celle du test de fisher pour les motifs non fixé est de 1.176369110^{-7}`

total_svbF7_unbound_H3K27me3 =  1863+ 0
total_svbF7_bound_H3K27me3 = 410 + 76

percent_svbf7_H3K27me3_unbound = round(100 * 1863 / total_svbF7_unbound_H3K27me3, 2 ) 
percent_svbf7_unbound_without_H3K27me3 = round(100 * 0 / total_svbF7_unbound_H3K27me3, 2 ) 

percent_svbf7_H3K27me3_bound = round(100 * 410 / total_svbF7_bound_H3K27me3, 2 ) 
percent_svbf7_bound_without_H3K27me3 = round(100 * 76 / total_svbF7_bound_H3K27me3, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K27me3_absent = c(percent_svbf7_bound_without_H3K27me3,percent_svbf7_unbound_without_H3K27me3)

H3K27me3_present = c(percent_svbf7_H3K27me3_bound,percent_svbf7_H3K27me3_unbound)

data_H3K27me3_act = data.frame(etat_svb,H3K27me3_absent,H3K27me3_present)
datatable(data_H3K27me3_act,rownames = F,filter = "none",colnames = c("Regions without H3K27me3","Regions with H3K27me3" ))

fig2 = plot_ly(data_H3K27me3_act, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_present, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb ( Act condition ) ", barmode = "stack")
fig2

fig2 = plot_ly(data_H3K27me3_act, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_present, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb ( Act condition ) ")
fig2

1.2.3.4.3 Rep

	in H3K27me3	absent H3K27me3
in svbF7 unbound	1878	0
absent svbF7 unbound	13423	0

	in H3K27me3	absent H3K27me3
in svbF7 bound	452	34
absent svbF7 bound	14849	0

pvalue_fixe = phyper(452 - 1, 486, 15335 - 486, 15301)
pvalue_nonfixe = phyper(1878 - 1, 1389, 15301 - 1389, 15301)
presence = c(452,14849)
absence = c(34,0)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27me3_fixe)

presence = c(1878,13423)
absence = c(0,0)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 0 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 3.427962810^{-52} Celle du test de fisher pour les motifs non fixé est de 1`

total_svbF7_unbound_H3K27me3 =  1878+ 0
total_svbF7_bound_H3K27me3 = 452 + 34

percent_svbf7_H3K27me3_unbound = round(100 * 1878 / total_svbF7_unbound_H3K27me3, 2 ) 
percent_svbf7_unbound_without_H3K27me3 = round(100 * 0 / total_svbF7_unbound_H3K27me3, 2 ) 

percent_svbf7_H3K27me3_bound = round(100 * 452 / total_svbF7_bound_H3K27me3, 2 ) 
percent_svbf7_bound_without_H3K27me3 = round(100 * 34 / total_svbF7_bound_H3K27me3, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K27me3_absent = c(percent_svbf7_bound_without_H3K27me3,percent_svbf7_unbound_without_H3K27me3)

H3K27me3_present = c(percent_svbf7_H3K27me3_bound,percent_svbf7_H3K27me3_unbound)

data_H3K27me3_rep = data.frame(etat_svb,H3K27me3_absent,H3K27me3_present)
datatable(data_H3K27me3_rep,rownames = F,filter = "none",colnames = c("Regions without H3K27me3","Regions with H3K27me3" ))

fig2 = plot_ly(data_H3K27me3_rep, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb ( Rep condition ) ", barmode = "stack")
fig2

fig2 = plot_ly(data_H3K27me3_rep, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb ( Rep condition ) ")
fig2

1.2.4 Pics associés aux gènes cibles directes de Svb

Je regarde ici l’enrichisement en histones des motifs de Svb en présence des différentes formes de Svb ( Act/ Rep) et Ctrl sur les pics associés aux gènes cibles directes

1.2.4.1 H3K4me1

1.2.4.1.1 Ctrl

	in H3K4me1	absent H3K4me1
in svbF7 unbound	1415	372
absent svbF7 unbound	8771	4422

	in H3K4me1	absent H3K4me1
in svbF7 bound	67	0
absent svbF7 bound	10129	5142

pvalue_fixe = phyper(67 - 1, 45, 15338 - 45, 10196)
pvalue_nonfixe = phyper(1415 - 1, 1787, 14980 - 1787, 10186)
presence = c(67,10129)
absence = c(0,5142)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me1_fixe_ctrl = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me1_fixe_ctrl)

presence = c(1415,8771)
absence = c(372,4422)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me1_nonfixe_ctrl = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me1_nonfixe_ctrl)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 2.286749710^{-12} Celle du test de fisher pour les motifs non fixé est de 6.388705810^{-29}`

total_svbF7_unbound = 1415 + 372
total_svbF7_bound = 67 + 0

percent_svbf7_H3K4me1_unbound = round(100 * 1415 / total_svbF7_unbound, 2 ) 
percent_svbf7_unbound_without_H3K4me1 = round(100 * 372 / total_svbF7_unbound, 2 ) 

percent_svbf7_H3K4me1_bound = round(100 * 67 / total_svbF7_bound, 2 ) 
percent_svbf7_bound_without_H3K4me1 = round(100 * 0 / total_svbF7_bound, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me1_absent = c(percent_svbf7_bound_without_H3K4me1,percent_svbf7_unbound_without_H3K4me1)

H3K4me1_present = c(percent_svbf7_H3K4me1_bound,percent_svbf7_H3K4me1_unbound)

data_H3K4me1_ctrl = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1_ctrl,rownames = F,filter = "none", colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig3 = plot_ly(data_H3K4me1_ctrl, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Ctrl condition)", barmode = "stack")
fig3

fig3 = plot_ly(data_H3K4me1_ctrl, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Ctrl condition)")
fig3

1.2.4.1.2 Act

	in H3K4me1	absent H3K4me1
in svbF7 unbound	1637	150
absent svbF7 unbound	10600	3396

	in H3K4me1	absent H3K4me1
in svbF7 bound	69	0
absent svbF7 bound	12181	3930

pvalue_fixe = phyper(68 - 1, 45, 16180 -45, 12250)
pvalue_nonfixe = phyper(1637 - 1, 1787, 15783 - 1787, 12237)
presence = c(60,12181)
absence = c(0,3930)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me1_fixe_act = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me1_fixe_act)

presence = c(1637,10600)
absence = c(150,3396)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me1_nonfixe_act = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me1_nonfixe_act)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 7.216512910^{-8} Celle du test de fisher pour les motifs non fixé est de 1.09857510^{-61}`

total_svbF7_unbound = 1637 + 150
total_svbF7_bound = 69 + 0

percent_svbf7_H3K4me1_unbound = round(100 * 1637 / total_svbF7_unbound, 2 ) 
percent_svbf7_unbound_without_H3K4me1 = round(100 * 150 / total_svbF7_unbound, 2 ) 

percent_svbf7_H3K4me1_bound = round(100 * 69 / total_svbF7_bound, 2 ) 
percent_svbf7_bound_without_H3K4me1 = round(100 * 0 / total_svbF7_bound, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me1_absent = c(percent_svbf7_bound_without_H3K4me1,percent_svbf7_unbound_without_H3K4me1)

H3K4me1_present = c(percent_svbf7_H3K4me1_bound,percent_svbf7_H3K4me1_unbound)

data_H3K4me1_act = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1_act,rownames = F,filter = "none", colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig3 = plot_ly(data_H3K4me1_act, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Act condition)", barmode = "stack")
fig3

fig3 = plot_ly(data_H3K4me1_act, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Act condition)")
fig3

1.2.4.1.3 Rep

	in H3K4me1	absent H3K4me1
in svbF7 unbound	1563	224
absent svbF7 unbound	10231	3579

	in H3K4me1	absent H3K4me1
in svbF7 unbound	65	0
absent svbF7 unbound	11737	4182

pvalue_fixe = phyper(65 - 1, 45, 15984 - 45, 11802)
pvalue_nonfixe = phyper(1563 - 1, 1787, 15597 - 1787, 11794)
presence = c(65,11737)
absence = c(0,4182)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me1_fixe_rep = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me1_fixe_rep)

presence = c(1563,10231)
absence = c(224,3579)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me1_nonfixe_rep = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me1_nonfixe_rep)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 4.59151510^{-9} Celle du test de fisher pour les motifs non fixé est de 1.23021910^{-39}`

total_svbF7_unbound = 1563 + 224
total_svbF7_bound = 65 + 0

percent_svbf7_H3K4me1_unbound = round(100 * 1563 / total_svbF7_unbound, 2 ) 
percent_svbf7_unbound_without_H3K4me1 = round(100 * 224 / total_svbF7_unbound, 2 ) 

percent_svbf7_H3K4me1_bound = round(100 * 65 / total_svbF7_bound, 2 ) 
percent_svbf7_bound_without_H3K4me1 = round(100 * 0 / total_svbF7_bound, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me1_absent = c(percent_svbf7_bound_without_H3K4me1,percent_svbf7_unbound_without_H3K4me1)

H3K4me1_present = c(percent_svbf7_H3K4me1_bound,percent_svbf7_H3K4me1_unbound)

data_H3K4me1_rep = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1_rep,rownames = F,filter = "none", colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig3 = plot_ly(data_H3K4me1_rep, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Rep condition)", barmode = "stack")
fig3

fig3 = plot_ly(data_H3K4me1_rep, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Rep condition)")
fig3

1.2.4.2 H3K4me3

1.2.4.2.1 Ctrl

	in H3K4me3	absent H3K4me3
in svbF7 unbound	1080	707
absent svbF7 unbound	9565	6548

	in H3K4me3	absent H3K4me3
in svbF7 bound	33	12
absent svbF7 bound	10612	7753

pvalue_fixe = phyper(33 - 1, 45, 18410 - 45, 10645)
pvalue_nonfixe = phyper(1080 - 1, 1787, 17900 - 1787, 10645)
presence = c(33,10612)
absence = c(12,7753)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me3_fixe_ctrl = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me3_fixe_ctrl)

presence = c(1080,956)
absence = c(707,6548)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me3_nonfixe_ctrl = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me3_nonfixe_ctrl)

La pvalue du test de hypergéométrique pour les motifs fixé est de 0.9768974 Celle du test de hypergéométrique pour les motifs non fixé est de 0.8028997

La pvalue du test de fisher pour les motifs fixé est de 0.0350842 Celle du test de fisher pour les motifs non fixé est de 0`

total_svbF7_unbound_H3K4me3 = 1080 + 707
total_svbF7_bound_H3K4me3 = 33 + 12

percent_svbf7_H3K4me3_unbound = round(100 * 1080 / total_svbF7_unbound_H3K4me3, 2 ) 
percent_svbf7_unbound_without_H3K4me3 = round(100 * 707 / total_svbF7_unbound_H3K4me3, 2 ) 

percent_svbf7_H3K4me3_bound = round(100 * 33 / total_svbF7_bound_H3K4me3, 2 ) 
percent_svbf7_bound_without_H3K4me3 = round(100 * 12 / total_svbF7_bound_H3K4me3, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me3_absent = c(percent_svbf7_bound_without_H3K4me3,percent_svbf7_unbound_without_H3K4me3)

H3K4me3_present = c(percent_svbf7_H3K4me3_bound,percent_svbf7_H3K4me3_unbound)

data_H3K4me3_ctrl = data.frame(etat_svb,H3K4me3_absent,H3K4me3_present)
datatable(data_H3K4me3_ctrl,rownames = F,filter = "none",colnames = c("Regions without H3K4me3","Regions with H3K4me3" ))

fig4 = plot_ly(data_H3K4me3_ctrl, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb (Ctrl condition)", barmode = "stack")
fig4

fig4 = plot_ly(data_H3K4me3_ctrl, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb (Ctrl condition)")
fig4

1.2.4.2.2 Act

	in H3K4me3	absent H3K4me3
in svbF7 unbound	638	1149
absent svbF7 unbound	6601	9605

	in H3K4me3	absent H3K4me3
in svbF7 bound	33	12
absent svbF7 bound	7207	11257

pvalue_fixe = phyper(33 - 1, 45, 18509 - 45, 7240)
pvalue_nonfixe = phyper(638 - 1, 1787, 17993 - 1787, 7239)
presence = c(33,7207)
absence = c(12,11257)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me3_fixe_act = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me3_fixe_act)

presence = c(638,6601)
absence = c(1149,9605)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me3_nonfixe_act = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me3_nonfixe_act)

La pvalue du test de hypergéométrique pour les motifs fixé est de 0.9999967 Celle du test de hypergéométrique pour les motifs non fixé est de 1.558858610^{-5}

La pvalue du test de fisher pour les motifs fixé est de 4.11252610^{-6} Celle du test de fisher pour les motifs non fixé est de 3.792336310^{-5}`

total_svbF7_unbound_H3K4me3 = 638 + 1149
total_svbF7_bound_H3K4me3 = 33 + 12

percent_svbf7_H3K4me3_unbound = round(100 * 638 / total_svbF7_unbound_H3K4me3, 2 ) 
percent_svbf7_unbound_without_H3K4me3 = round(100 * 1149 / total_svbF7_unbound_H3K4me3, 2 ) 

percent_svbf7_H3K4me3_bound = round(100 * 33 / total_svbF7_bound_H3K4me3, 2 ) 
percent_svbf7_bound_without_H3K4me3 = round(100 * 12 / total_svbF7_bound_H3K4me3, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me3_absent = c(percent_svbf7_bound_without_H3K4me3,percent_svbf7_unbound_without_H3K4me3)

H3K4me3_present = c(percent_svbf7_H3K4me3_bound,percent_svbf7_H3K4me3_unbound)

data_H3K4me3_act = data.frame(etat_svb,H3K4me3_absent,H3K4me3_present)
datatable(data_H3K4me3_act,rownames = F,filter = "none",colnames = c("Regions without H3K4me3","Regions with H3K4me3" ))

fig4 = plot_ly(data_H3K4me3_act, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb (Act condition)", barmode = "stack")
fig4

fig4 = plot_ly(data_H3K4me3_act, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb (Act condition)")
fig4

1.2.4.2.3 Rep

	in H3K4me3	absent H3K4me3
in svbF7 unbound	1263	524
absent svbF7 unbound	10598	6133

	in H3K4me3	absent H3K4me3
in svbF7 bound	32	13
absent svbF7 bound	4763	12483

pvalue_fixe = phyper(32 - 1, 45, 17291 - 45, 4795)
pvalue_nonfixe = phyper(1263 - 1, 1787, 18518 - 1787, 11861)
presence = c(32,4763)
absence = c(13,12483)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me3_fixe_rep = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me3_fixe_rep)

presence = c(1263,524)
absence = c(10598,6133 )
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me3_nonfixe_rep = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me3_nonfixe_rep)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 1.783416710^{-9} Celle du test de fisher pour les motifs non fixé est de 5.398360210^{-10}`

total_svbF7_unbound_H3K4me3 = 1263 + 524
total_svbF7_bound_H3K4me3 = 32 + 13

percent_svbf7_H3K4me3_unbound = round(100 * 1263 / total_svbF7_unbound_H3K4me3, 2 ) 
percent_svbf7_unbound_without_H3K4me3 = round(100 * 524 / total_svbF7_unbound_H3K4me3, 2 ) 

percent_svbf7_H3K4me3_bound = round(100 * 32 / total_svbF7_bound_H3K4me3, 2 ) 
percent_svbf7_bound_without_H3K4me3 = round(100 * 13 / total_svbF7_bound_H3K4me3, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me3_absent = c(percent_svbf7_bound_without_H3K4me3,percent_svbf7_unbound_without_H3K4me3)

H3K4me3_present = c(percent_svbf7_H3K4me3_bound,percent_svbf7_H3K4me3_unbound)

data_H3K4me3_rep = data.frame(etat_svb,H3K4me3_absent,H3K4me3_present)
datatable(data_H3K4me3_rep,rownames = F,filter = "none",colnames = c("Regions without H3K4me3","Regions with H3K4me3" ))

fig4 = plot_ly(data_H3K4me3_rep, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb (Rep condition)", barmode = "stack")
fig4

fig4 = plot_ly(data_H3K4me3_rep, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb (Rep condition)")
fig4

1.2.4.2.4 Rapport K4me1/ K4me3

1.2.4.2.4.1 Ctrl

total_K4me1_S2 = 8782
total_K4me3_S2 = 4812
rapp_fixe = round((contingence_H3K4me1_fixe_ctrl[1,1] / total_K4me1_S2 ) / (contingence_H3K4me3_fixe_ctrl[1,1] / total_K4me3_S2),2)
rapp_nonfixe = round((contingence_H3K4me1_nonfixe_ctrl[1,1] / total_K4me1_S2)  / (contingence_H3K4me3_nonfixe_ctrl[1,1] / total_K4me3_S2) ,2)

Il y a 1.11 fois de H3K4me1 que de H3K4me3 fixé.

Il y a 0.72 fois de H3K4me1 que de H3K4me3 non fixé.

1.2.4.2.4.2 Act

total_K4me1_S2 = 8782
total_K4me3_S2 = 4812
rapp_fixe = round((contingence_H3K4me1_fixe_act [1,1] / total_K4me1_S2 ) / (contingence_H3K4me3_fixe_act[1,1] / total_K4me3_S2),2)
rapp_nonfixe = round((contingence_H3K4me1_nonfixe_act[1,1] / total_K4me1_S2)  / (contingence_H3K4me3_nonfixe_act[1,1] / total_K4me3_S2) ,2)

Il y a 1 fois de H3K4me1 que de H3K4me3 fixé.

Il y a 1.41 fois de H3K4me1 que de H3K4me3 non fixé.

1.2.4.2.4.3 Rep

total_K4me1_S2 = 8782
total_K4me3_S2 = 4812
rapp_fixe = round((contingence_H3K4me1_fixe_rep[1,1] / total_K4me1_S2 ) / (contingence_H3K4me3_fixe_rep[1,1] / total_K4me3_S2),2)
rapp_nonfixe = round((contingence_H3K4me1_nonfixe_rep[1,1] / total_K4me1_S2)  / (contingence_H3K4me3_nonfixe_rep[1,1] / total_K4me3_S2) ,2)

1.2.4.3 H3K27ac

1.2.4.3.1 Ctrl

	in H3K27ac	absent H3K27ac
in svbF7 unbound	602	1185
absent svbF7 unbound	5029	8890

	in H3K27ac	absent H3K27ac
in svbF7 bound	43	2
absent svbF7 bound	5592	10461

pvalue_fixe = phyper(43 - 1, 45, 16098 - 45, 5635)
pvalue_nonfixe = phyper(602 - 1, 1787, 15706 - 1787, 5631)
presence = c(43,5592)
absence = c(2,10461)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27ac_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27ac_fixe)

presence = c(602,5029)
absence = c(1185,8890)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27ac_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 0.019714

La pvalue du test de fisher pour les motifs fixé est de 9.685414710^{-18} Celle du test de fisher pour les motifs non fixé est de 0.0436541`

total_svbF7_unbound_H3K27ac = 602 + 1185
total_svbF7_bound_H3K27ac = 43 + 2

percent_svbf7_H3K27ac_unbound = round(100 * 602 / total_svbF7_unbound_H3K27ac, 2 ) 
percent_svbf7_unbound_without_H3K27ac = round(100 * 1185 / total_svbF7_unbound_H3K27ac, 2 ) 

percent_svbf7_H3K27ac_bound = round(100 * 43 / total_svbF7_bound_H3K27ac, 2 ) 
percent_svbf7_bound_without_H3K27ac = round(100 * 2 / total_svbF7_bound_H3K27ac, 2 ) 


etat_svb = c("svbF7_bound","svb_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)



etat_svb = c("svbF7_bound","svb_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)

H3K27ac_present = c(percent_svbf7_H3K27ac_bound,percent_svbf7_H3K27ac_unbound)

data_H3K27ac_ctrl = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac_ctrl,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

fig1 = plot_ly(data_H3K27ac_ctrl, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_present, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Ctrl Condtion) ", barmode = "stack")
fig1

fig1 = plot_ly(data_H3K27ac_ctrl, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_present, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Ctrl Condtion) ")
fig1

1.2.4.3.2 Act

	in H3K27ac	absent H3K27ac
in svbF7 unbound	596	1191
absent svbF7 unbound	5081	8995

	in H3K27ac	absent H3K27ac
in svbF7 bound	43	2
absent svbF7 bound	5638	10579

pvalue_fixe = phyper(43 - 1, 45, 16262 - 45, 5681)
pvalue_nonfixe = phyper(596 - 1, 1787, 15863 - 1787, 5677)
presence = c(43,5638)
absence = c(2,10579)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27ac_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27ac_fixe)

presence = c(596,5081)
absence = c(1191,8995)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27ac_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 0.0103025

La pvalue du test de fisher pour les motifs fixé est de 8.909640810^{-18} Celle du test de fisher pour les motifs non fixé est de 0.0226837`

total_svbF7_unbound_H3K27ac = 596 + 1191
total_svbF7_bound_H3K27ac = 43 + 2

percent_svbf7_H3K27ac_unbound = round(100 * 596 / total_svbF7_unbound_H3K27ac, 2 ) 
percent_svbf7_unbound_without_H3K27ac = round(100 * 1191 / total_svbF7_unbound_H3K27ac, 2 ) 

percent_svbf7_H3K27ac_bound = round(100 * 43 / total_svbF7_bound_H3K27ac, 2 ) 
percent_svbf7_bound_without_H3K27ac = round(100 * 2 / total_svbF7_bound_H3K27ac, 2 ) 


etat_svb = c("svbF7_bound","svb_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)

H3K27ac_present = c(percent_svbf7_H3K27ac_bound,percent_svbf7_H3K27ac_unbound)

data_H3K27ac_act = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac_act,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

fig1 = plot_ly(data_H3K27ac_act, x = ~etat_svb,y = ~H3K27ac_absent, type = "bar",name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_present, name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Act Condtion) ", barmode = "stack")
fig1

fig1 = plot_ly(data_H3K27ac_act, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Act Condtion) ", barmode = "stack")
fig1

fig1 = plot_ly(data_H3K27ac_act, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Act Condtion) ")
fig1

1.2.4.3.3 Rep

	in H3K27ac	absent H3K27ac
in svbF7 unbound	594	1193
absent svbF7 unbound	5037	8903

	in H3K27ac	absent H3K27ac
in svbF7 bound	38	7
absent svbF7 bound	5596	10479

pvalue_fixe = phyper(38 - 1, 45, 16120 - 45, 5634)
pvalue_nonfixe = phyper(594 - 1, 1787, 15727 - 1787, 5631)
presence = c(38,5596)
absence = c(7,10479)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27ac_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27ac_fixe)

presence = c(594,5037)
absence = c(1193,8903)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27ac_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 0.007383

La pvalue du test de fisher pour les motifs fixé est de 1.035723410^{-11} Celle du test de fisher pour les motifs non fixé est de 0.0170752`

total_svbF7_unbound_H3K27ac = 594 + 1193
total_svbF7_bound_H3K27ac = 38 + 7

percent_svbf7_H3K27ac_unbound = round(100 * 594 / total_svbF7_unbound_H3K27ac, 2 ) 
percent_svbf7_unbound_without_H3K27ac = round(100 * 1193 / total_svbF7_unbound_H3K27ac, 2 ) 

percent_svbf7_H3K27ac_bound = round(100 * 38 / total_svbF7_bound_H3K27ac, 2 ) 
percent_svbf7_bound_without_H3K27ac = round(100 * 7 / total_svbF7_bound_H3K27ac, 2 ) 


etat_svb = c("svbF7_bound","svb_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)

H3K27ac_present = c(percent_svbf7_H3K27ac_bound,percent_svbf7_H3K27ac_unbound)

data_H3K27ac_rep = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac_rep,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

fig1 = plot_ly(data_H3K27ac_rep, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Rep condition) ", barmode = "stack")
fig1

fig1 = plot_ly(data_H3K27ac_rep, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Rep condition) ")
fig1

1.2.4.4 H3K27me3

1.2.4.4.1 Ctrl

	in H3K27me3	absent H3K27me3
in svbF7 unbound	2268	0
absent svbF7 unbound	12859	0

	in H3K27me3	absent H3K27me3
in svbF7 bound	43	2
absent svbF7 bound	15084	220

pvalue_fixe = phyper(43 - 1, 45, 15349 - 45, 15127)
pvalue_nonfixe = phyper(2268 - 1, 1787, 15127 - 1787, 15127)
presence = c(43,15084)
absence = c(2,220)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27me3_fixe)

presence = c(2268,15084)
absence = c(0,0)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 0.0271347 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 0.1378574 Celle du test de fisher pour les motifs non fixé est de 1`

total_svbF7_unbound_H3K27me3 = 2268 + 0
total_svbF7_bound_H3K27me3 = 43 + 2

percent_svbf7_H3K27me3_unbound = round(100 * 2268 / total_svbF7_unbound_H3K27me3, 2 ) 
percent_svbf7_unbound_without_H3K27me3 = round(100 * 0 / total_svbF7_unbound_H3K27me3, 2 ) 

percent_svbf7_H3K27me3_bound = round(100 * 43 / total_svbF7_bound_H3K27me3, 2 ) 
percent_svbf7_bound_without_H3K27me3 = round(100 * 2 / total_svbF7_bound_H3K27me3, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K27me3_absent = c(percent_svbf7_bound_without_H3K27me3,percent_svbf7_unbound_without_H3K27me3)

H3K27me3_present = c(percent_svbf7_H3K27me3_bound,percent_svbf7_H3K27me3_unbound)

data_H3K27me3_ctrl = data.frame(etat_svb,H3K27me3_absent,H3K27me3_present)
datatable(data_H3K27me3_ctrl,rownames = F,filter = "none",colnames = c("Regions without H3K27me3","Regions with H3K27me3" ))

fig2 = plot_ly(data_H3K27me3_ctrl, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb (Ctrl condition)", barmode = "stack")
fig2

fig2 = plot_ly(data_H3K27me3_ctrl, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb (Ctrl condition)")
fig2

1.2.4.4.2 Act

	in H3K27me3	absent H3K27me3
in svbF7 unbound	2180	0
absent svbF7 unbound	12129	19

	in H3K27me3	absent H3K27me3
in svbF7 bound	46	0
absent svbF7 bound	14263	344

pvalue_fixe = phyper(46 - 1, 45, 14653 - 45, 14309)
pvalue_nonfixe = phyper(2180 - 1, 1787, 14328 - 1787, 14328)
presence = c(46,14263)
absence = c(0,19)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27me3_fixe)

presence = c(2180,12129)
absence = c(0,19)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 1 Celle du test de fisher pour les motifs non fixé est de 0.1002134`

total_svbF7_unbound_H3K27me3 =  2180+ 0
total_svbF7_bound_H3K27me3 = 46 + 0

percent_svbf7_H3K27me3_unbound = round(100 * 2180 / total_svbF7_unbound_H3K27me3, 2 ) 
percent_svbf7_unbound_without_H3K27me3 = round(100 * 0 / total_svbF7_unbound_H3K27me3, 2 ) 

percent_svbf7_H3K27me3_bound = round(100 * 46 / total_svbF7_bound_H3K27me3, 2 ) 
percent_svbf7_bound_without_H3K27me3 = round(100 * 0 / total_svbF7_bound_H3K27me3, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K27me3_absent = c(percent_svbf7_bound_without_H3K27me3,percent_svbf7_unbound_without_H3K27me3)

H3K27me3_present = c(percent_svbf7_H3K27me3_bound,percent_svbf7_H3K27me3_unbound)

data_H3K27me3_act = data.frame(etat_svb,H3K27me3_absent,H3K27me3_present)
datatable(data_H3K27me3_act,rownames = F,filter = "none",colnames = c("Regions without H3K27me3","Regions with H3K27me3" ))

fig2 = plot_ly(data_H3K27me3_act, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb ( Act condition ) ", barmode = "stack")
fig2

fig2 = plot_ly(data_H3K27me3_act, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb ( Act condition ) ")
fig2

1.2.4.4.3 Rep

	in H3K27me3	absent H3K27me3
in svbF7 unbound	223	0
absent svbF7 unbound	13048	0

	in H3K27me3	absent H3K27me3
in svbF7 bound	48	0
absent svbF7 bound	15253	0

pvalue_fixe = phyper(48 - 1, 45, 15301 - 45, 15301)
pvalue_nonfixe = phyper(2253 - 1, 1787, 15301 - 1787, 15301)
presence = c(48,15253)
absence = c(0,0)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27me3_fixe)

presence = c(223,13048)
absence = c(0,0)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 1 Celle du test de fisher pour les motifs non fixé est de 1`

total_svbF7_unbound_H3K27me3 =  223+ 0
total_svbF7_bound_H3K27me3 = 48 + 0

percent_svbf7_H3K27me3_unbound = round(100 * 223 / total_svbF7_unbound_H3K27me3, 2 ) 
percent_svbf7_unbound_without_H3K27me3 = round(100 * 0 / total_svbF7_unbound_H3K27me3, 2 ) 

percent_svbf7_H3K27me3_bound = round(100 * 48 / total_svbF7_bound_H3K27me3, 2 ) 
percent_svbf7_bound_without_H3K27me3 = round(100 * 0 / total_svbF7_bound_H3K27me3, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K27me3_absent = c(percent_svbf7_bound_without_H3K27me3,percent_svbf7_unbound_without_H3K27me3)

H3K27me3_present = c(percent_svbf7_H3K27me3_bound,percent_svbf7_H3K27me3_unbound)

data_H3K27me3_rep = data.frame(etat_svb,H3K27me3_absent,H3K27me3_present)
datatable(data_H3K27me3_rep,rownames = F,filter = "none",colnames = c("Regions without H3K27me3","Regions with H3K27me3" ))

fig2 = plot_ly(data_H3K27me3_rep, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb ( Rep condition ) ", barmode = "stack")
fig2

fig2 = plot_ly(data_H3K27me3_rep, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb ( Rep condition ) ")
fig2

1.2.5 Sur les promoteurs

Je regarde ici l’enrichisement en histones des motifs de Svb en présence des différentes formes de Svb ( Act/ Rep) et Ctrl

1.2.5.1 H3K4me1

1.2.5.1.1 Ctrl

	in H3K4me1	absent H3K4me1
in svbF7 unbound	1434	366
absent svbF7 unbound	8757	4425

	in H3K4me1	absent H3K4me1
in svbF7 bound	48	0
absent svbF7 bound	10148	4328

pvalue_fixe = phyper(48 - 1, 30, 14524 - 30, 10196)
pvalue_nonfixe = phyper(1434 - 1, 1800, 14982 - 1800, 10191)
presence = c(48,10148)
absence = c(0,4328)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me1_fixe_ctrl = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me1_fixe_ctrl)

presence = c(1434,8757)
absence = c(366,4425)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me1_nonfixe_ctrl = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me1_nonfixe_ctrl)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 6.868377910^{-8} Celle du test de fisher pour les motifs non fixé est de 1.65729710^{-31}`

total_svbF7_unbound = 1434 + 366
total_svbF7_bound = 48 + 0

percent_svbf7_H3K4me1_unbound = round(100 * 1434 / total_svbF7_unbound, 2 ) 
percent_svbf7_unbound_without_H3K4me1 = round(100 * 366 / total_svbF7_unbound, 2 ) 

percent_svbf7_H3K4me1_bound = round(100 * 48 / total_svbF7_bound, 2 ) 
percent_svbf7_bound_without_H3K4me1 = round(100 * 0 / total_svbF7_bound, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me1_absent = c(percent_svbf7_bound_without_H3K4me1,percent_svbf7_unbound_without_H3K4me1)

H3K4me1_present = c(percent_svbf7_H3K4me1_bound,percent_svbf7_H3K4me1_unbound)

data_H3K4me1_ctrl = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1_ctrl,rownames = F,filter = "none", colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig3 = plot_ly(data_H3K4me1_ctrl, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_present, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Ctrl condition)", barmode = "stack")
fig3

fig3 = plot_ly(data_H3K4me1_ctrl, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_present, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Ctrl condition)")
fig3

1.2.5.1.2 Act

	in H3K4me1	absent H3K4me1
in svbF7 unbound	1657	143
absent svbF7 unbound	10587	3398

	in H3K4me1	absent H3K4me1
in svbF7 bound	47	0
absent svbF7 bound	12203	3028

pvalue_fixe = phyper(47 - 1, 30, 15278 - 30, 12250)
pvalue_nonfixe = phyper(1657 - 1, 1800, 15785 - 1800, 12244)
presence = c(1657,10587)
absence = c(143,3398)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me1_fixe_act = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me1_fixe_act)

presence = c(47,0)
absence = c(12203,3028)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me1_nonfixe_act = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me1_nonfixe_act)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 1.853204510^{-66} Celle du test de fisher pour les motifs non fixé est de 5.716693710^{-5}`

total_svbF7_unbound = 1657 + 143
total_svbF7_bound = 47 + 0

percent_svbf7_H3K4me1_unbound = round(100 * 1657 / total_svbF7_unbound, 2 ) 
percent_svbf7_unbound_without_H3K4me1 = round(100 * 143 / total_svbF7_unbound, 2 ) 

percent_svbf7_H3K4me1_bound = round(100 * 47 / total_svbF7_bound, 2 ) 
percent_svbf7_bound_without_H3K4me1 = round(100 * 0 / total_svbF7_bound, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me1_absent = c(percent_svbf7_bound_without_H3K4me1,percent_svbf7_unbound_without_H3K4me1)

H3K4me1_present = c(percent_svbf7_H3K4me1_bound,percent_svbf7_H3K4me1_unbound)

data_H3K4me1_act = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1_act,rownames = F,filter = "none", colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig3 = plot_ly(data_H3K4me1_act, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Act condition)", barmode = "stack")
fig3

fig3 = plot_ly(data_H3K4me1_act, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Act condition)")
fig3

1.2.5.1.3 Rep

	in H3K4me1	absent H3K4me1
in svbF7 unbound	1580	220
absent svbF7 unbound	10217	3582

	in H3K4me1	absent H3K4me1
in svbF7 bound	46	0
absent svbF7 bound	11756	3301

pvalue_fixe = phyper(46 - 1, 30, 15103 - 30, 11802)
pvalue_nonfixe = phyper(1508 - 1, 1800, 15599 - 1800, 11797)
presence = c(46,11756)
absence = c(0,3301)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me1_fixe_rep = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me1_fixe_rep)

presence = c(1580,10217)
absence = c(220,3582)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me1_nonfixe_rep = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me1_nonfixe_rep)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 1.783063810^{-5} Celle du test de fisher pour les motifs non fixé est de 4.847625110^{-42}`

total_svbF7_unbound = 1580 + 220
total_svbF7_bound = 46 + 0

percent_svbf7_H3K4me1_unbound = round(100 * 1580 / total_svbF7_unbound, 2 ) 
percent_svbf7_unbound_without_H3K4me1 = round(100 * 220 / total_svbF7_unbound, 2 ) 

percent_svbf7_H3K4me1_bound = round(100 * 46 / total_svbF7_bound, 2 ) 
percent_svbf7_bound_without_H3K4me1 = round(100 * 0 / total_svbF7_bound, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me1_absent = c(percent_svbf7_bound_without_H3K4me1,percent_svbf7_unbound_without_H3K4me1)

H3K4me1_present = c(percent_svbf7_H3K4me1_bound,percent_svbf7_H3K4me1_unbound)

data_H3K4me1_rep = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1_rep,rownames = F,filter = "none", colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig3 = plot_ly(data_H3K4me1_rep, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Rep condition)", barmode = "stack")
fig3

fig3 = plot_ly(data_H3K4me1_rep, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb (Rep condition)")
fig3

1.2.5.2 H3K4me3

1.2.5.2.1 Ctrl

	in H3K4me3	absent H3K4me3
in svbF7 unbound	1087	713
absent svbF7 unbound	9558	6543

	in H3K4me3	absent H3K4me3
in svbF7 bound	31	0
absent svbF7 bound	10614	6606

pvalue_fixe = phyper(31 - 1, 30, 17251 - 30, 10645)
pvalue_nonfixe = phyper(1087 - 1, 1800, 17901 - 1800, 10645)
presence = c(31,10614)
absence = c(0,6606)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me3_fixe_ctrl = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me3_fixe_ctrl)

presence = c(1087,9558)
absence = c(713,6543)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me3_nonfixe_ctrl = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me3_nonfixe_ctrl)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 0.7925253

La pvalue du test de fisher pour les motifs fixé est de 5.534338610^{-7} Celle du test de fisher pour les motifs non fixé est de 0.4037`

total_svbF7_unbound_H3K4me3 = 1087 + 713
total_svbF7_bound_H3K4me3 = 31 + 0

percent_svbf7_H3K4me3_unbound = round(100 * 1087 / total_svbF7_unbound_H3K4me3, 2 ) 
percent_svbf7_unbound_without_H3K4me3 = round(100 * 713 / total_svbF7_unbound_H3K4me3, 2 ) 

percent_svbf7_H3K4me3_bound = round(100 * 31 / total_svbF7_bound_H3K4me3, 2 ) 
percent_svbf7_bound_without_H3K4me3 = round(100 * 0 / total_svbF7_bound_H3K4me3, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me3_absent = c(percent_svbf7_bound_without_H3K4me3,percent_svbf7_unbound_without_H3K4me3)

H3K4me3_present = c(percent_svbf7_H3K4me3_bound,percent_svbf7_H3K4me3_unbound)

data_H3K4me3_ctrl = data.frame(etat_svb,H3K4me3_absent,H3K4me3_present)
datatable(data_H3K4me3_ctrl,rownames = F,filter = "none",colnames = c("Regions without H3K4me3","Regions with H3K4me3" ))

fig4 = plot_ly(data_H3K4me3_ctrl, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb (Ctrl condition)", barmode = "stack")
fig4

fig4 = plot_ly(data_H3K4me3_ctrl, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb (Ctrl condition)")
fig4

1.2.5.2.2 Act

	in H3K4me3	absent H3K4me3
in svbF7 unbound	646	1154
absent svbF7 unbound	6593	9601

	in H3K4me3	absent H3K4me3
in svbF7 bound	28	2
absent svbF7 bound	7212	10096

pvalue_fixe = phyper(28 - 1, 30, 17338 - 30, 7240)
pvalue_nonfixe = phyper(646 - 1, 1800, 17994 - 1800, 7239)
presence = c(28,7212)
absence = c(2,10096)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me3_fixe_act = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me3_fixe_act)

presence = c(646,6593)
absence = c(2,10096)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me3_nonfixe_act = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me3_nonfixe_act)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 3.076336810^{-5}

La pvalue du test de fisher pour les motifs fixé est de 3.624050310^{-9} Celle du test de fisher pour les motifs non fixé est de 1.811466310^{-248}`

total_svbF7_unbound_H3K4me3 = 646 + 1154
total_svbF7_bound_H3K4me3 = 28 + 2

percent_svbf7_H3K4me3_unbound = round(100 * 646 / total_svbF7_unbound_H3K4me3, 2 ) 
percent_svbf7_unbound_without_H3K4me3 = round(100 * 1154 / total_svbF7_unbound_H3K4me3, 2 ) 

percent_svbf7_H3K4me3_bound = round(100 * 28 / total_svbF7_bound_H3K4me3, 2 ) 
percent_svbf7_bound_without_H3K4me3 = round(100 * 2/ total_svbF7_bound_H3K4me3, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me3_absent = c(percent_svbf7_bound_without_H3K4me3,percent_svbf7_unbound_without_H3K4me3)

H3K4me3_present = c(percent_svbf7_H3K4me3_bound,percent_svbf7_H3K4me3_unbound)

data_H3K4me3_act = data.frame(etat_svb,H3K4me3_absent,H3K4me3_present)
datatable(data_H3K4me3_act,rownames = F,filter = "none",colnames = c("Regions without H3K4me3","Regions with H3K4me3" ))

fig4 = plot_ly(data_H3K4me3_act, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb (Act condition)", barmode = "stack")
fig4

fig4 = plot_ly(data_H3K4me3_act, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb (Act condition)")
fig4

1.2.5.2.3 Rep

	in H3K4me3	absent H3K4me3
in svbF7 unbound	444	1356
absent svbF7 unbound	4350	10691

	in H3K4me3	absent H3K4me3
in svbF7 bound	28	2
absent svbF7 bound	4764	11467

pvalue_fixe = phyper(28 - 1, 30, 16264 - 30, 4795)
pvalue_nonfixe = phyper(444 - 1, 1800, 16841 - 1800, 4794)
presence = c(444,4350)
absence = c(1356,10691)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me3_fixe_rep = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me3_fixe_rep)

presence = c(28,2)
absence = c(4764,11467)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me3_nonfixe_rep = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me3_nonfixe_rep)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 5.822629410^{-5}

La pvalue du test de fisher pour les motifs fixé est de 1.362101910^{-4} Celle du test de fisher pour les motifs non fixé est de 2.932863510^{-13}`

total_svbF7_unbound_H3K4me3 = 444 + 1356
total_svbF7_bound_H3K4me3 = 28 + 2

percent_svbf7_H3K4me3_unbound = round(100 * 444 / total_svbF7_unbound_H3K4me3, 2 ) 
percent_svbf7_unbound_without_H3K4me3 = round(100 * 1356 / total_svbF7_unbound_H3K4me3, 2 ) 

percent_svbf7_H3K4me3_bound = round(100 * 28 / total_svbF7_bound_H3K4me3, 2 ) 
percent_svbf7_bound_without_H3K4me3 = round(100 * 2 / total_svbF7_bound_H3K4me3, 2 ) 



etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me3_absent = c(percent_svbf7_bound_without_H3K4me3,percent_svbf7_unbound_without_H3K4me3)

H3K4me3_present = c(percent_svbf7_H3K4me3_bound,percent_svbf7_H3K4me3_unbound)

data_H3K4me3_rep = data.frame(etat_svb,H3K4me3_absent,H3K4me3_present)
datatable(data_H3K4me3_rep,rownames = F,filter = "none",colnames = c("Regions without H3K4me3","Regions with H3K4me3" ))

fig4 = plot_ly(data_H3K4me3_rep, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb (Rep condition)", barmode = "stack")
fig4

fig4 = plot_ly(data_H3K4me3_rep, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig4 = fig4 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb (Rep condition)")
fig4

1.2.5.2.4 Rapport K4me1/ K4me3

1.2.5.2.4.1 Ctrl

total_K4me1_S2 = 8782
total_K4me3_S2 = 4812
rapp_fixe = round((contingence_H3K4me1_fixe_ctrl[1,1] / total_K4me1_S2 ) / (contingence_H3K4me3_fixe_ctrl[1,1] / total_K4me3_S2),2)
rapp_nonfixe = round((contingence_H3K4me1_nonfixe_ctrl[1,1] / total_K4me1_S2)  / (contingence_H3K4me3_nonfixe_ctrl[1,1] / total_K4me3_S2) ,2)

Il y a 0.85 fois de H3K4me1 que de H3K4me3 fixé.

Il y a 0.72 fois de H3K4me1 que de H3K4me3 non fixé.

1.2.5.2.4.2 Act

total_K4me1_S2 = 8782
total_K4me3_S2 = 4812
rapp_fixe = round((contingence_H3K4me1_fixe_act [1,1] / total_K4me1_S2 ) / (contingence_H3K4me3_fixe_act[1,1] / total_K4me3_S2),2)
rapp_nonfixe = round((contingence_H3K4me1_nonfixe_act[1,1] / total_K4me1_S2)  / (contingence_H3K4me3_nonfixe_act[1,1] / total_K4me3_S2) ,2)

Il y a 32.43 fois de H3K4me1 que de H3K4me3 fixé.

Il y a 0.04 fois de H3K4me1 que de H3K4me3 non fixé.

1.2.5.2.4.3 Rep

total_K4me1_S2 = 8782
total_K4me3_S2 = 4812
rapp_fixe = round((contingence_H3K4me1_fixe_rep[1,1] / total_K4me1_S2 ) / (contingence_H3K4me3_fixe_rep[1,1] / total_K4me3_S2),2)
rapp_nonfixe = round((contingence_H3K4me1_nonfixe_rep[1,1] / total_K4me1_S2)  / (contingence_H3K4me3_nonfixe_rep[1,1] / total_K4me3_S2) ,2)

1.2.5.3 H3K27ac

1.2.5.3.1 Ctrl

	in H3K27ac	absent H3K27ac
in svbF7 unbound	612	1188
absent svbF7 unbound	5020	8887

	in H3K27ac	absent H3K27ac
in svbF7 bound	34	0
absent svbF7 bound	5601	9570

pvalue_fixe = phyper(34 - 1, 30, 15205 - 30, 5635)
pvalue_nonfixe = phyper(612 - 1, 1800, 15707 - 1800, 5632)
presence = c(34,5601)
absence = c(0,9570)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27ac_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27ac_fixe)

presence = c(612,5020)
absence = c(1188,8887)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27ac_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 0.0378741

La pvalue du test de fisher pour les motifs fixé est de 2.068081110^{-15} Celle du test de fisher pour les motifs non fixé est de 0.0847469`

total_svbF7_unbound_H3K27ac = 612 + 1188
total_svbF7_bound_H3K27ac = 34 + 0

percent_svbf7_H3K27ac_unbound = round(100 * 612 / total_svbF7_unbound_H3K27ac, 2 ) 
percent_svbf7_unbound_without_H3K27ac = round(100 * 1188 / total_svbF7_unbound_H3K27ac, 2 ) 

percent_svbf7_H3K27ac_bound = round(100 * 34 / total_svbF7_bound_H3K27ac, 2 ) 
percent_svbf7_bound_without_H3K27ac = round(100 * 0 / total_svbF7_bound_H3K27ac, 2 ) 


etat_svb = c("svbF7_bound","svb_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)

H3K27ac_present = c(percent_svbf7_H3K27ac_bound,percent_svbf7_H3K27ac_unbound)

data_H3K27ac_ctrl = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac_ctrl,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

fig1 = plot_ly(data_H3K27ac_ctrl, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Ctrl condition)", barmode = "stack")
fig1

fig1 = plot_ly(data_H3K27ac_ctrl, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Ctrl condition)")
fig1

1.2.5.3.2 Act

	in H3K27ac	absent H3K27ac
in svbF7 unbound	606	1194
absent svbF7 unbound	5072	8991

	in H3K27ac	absent H3K27ac
in svbF7 bound	35	0
absent svbF7 bound	5646	9670

pvalue_fixe = phyper(35 - 1, 30, 15351 - 30, 5681)
pvalue_nonfixe = phyper(606 - 1, 1800, 15863 - 1800, 5678)
presence = c(35,5646)
absence = c(0,9670)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27ac_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27ac_fixe)

presence = c(606,5072)
absence = c(1194,8991)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27ac_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 0.021082

La pvalue du test de fisher pour les motifs fixé est de 7.26694110^{-16} Celle du test de fisher pour les motifs non fixé est de 0.0472022`

total_svbF7_unbound_H3K27ac = 606 + 1194
total_svbF7_bound_H3K27ac = 35 + 0

percent_svbf7_H3K27ac_unbound = round(100 * 606 / total_svbF7_unbound_H3K27ac, 2 ) 
percent_svbf7_unbound_without_H3K27ac = round(100 * 1194 / total_svbF7_unbound_H3K27ac, 2 ) 

percent_svbf7_H3K27ac_bound = round(100 * 35 / total_svbF7_bound_H3K27ac, 2 ) 
percent_svbf7_bound_without_H3K27ac = round(100 * 0 / total_svbF7_bound_H3K27ac, 2 ) 


etat_svb = c("svbF7_bound","svb_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)

H3K27ac_present = c(percent_svbf7_H3K27ac_bound,percent_svbf7_H3K27ac_unbound)

data_H3K27ac_act = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac_act,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

fig1 = plot_ly(data_H3K27ac_act, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Act Condtion) ", barmode = "stack")
fig1

fig1 = plot_ly(data_H3K27ac_act, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Act Condtion) ")
fig1

1.2.5.3.3 Rep

	in H3K27ac	absent H3K27ac
in svbF7 unbound	602	1198
absent svbF7 unbound	5030	8898

	in H3K27ac	absent H3K27ac
in svbF7 bound	31	0
absent svbF7 bound	5603	9590

pvalue_fixe = phyper(31 - 1, 30, 15224 - 30, 5634)
pvalue_nonfixe = phyper(602 - 1, 1800, 15728 - 1800, 5632)
presence = c(31,5603)
absence = c(0,9590)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27ac_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27ac_fixe)

presence = c(602,5030)
absence = c(1198,8898)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27ac_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 0.0119846

La pvalue du test de fisher pour les motifs fixé est de 3.929115610^{-14} Celle du test de fisher pour les motifs non fixé est de 0.0264009`

total_svbF7_unbound_H3K27ac = 602 + 1198
total_svbF7_bound_H3K27ac = 31 + 0

percent_svbf7_H3K27ac_unbound = round(100 * 602 / total_svbF7_unbound_H3K27ac, 2 ) 
percent_svbf7_unbound_without_H3K27ac = round(100 * 1198 / total_svbF7_unbound_H3K27ac, 2 ) 

percent_svbf7_H3K27ac_bound = round(100 * 31 / total_svbF7_bound_H3K27ac, 2 ) 
percent_svbf7_bound_without_H3K27ac = round(100 * 0 / total_svbF7_bound_H3K27ac, 2 ) 


etat_svb = c("svbF7_bound","svb_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)

H3K27ac_present = c(percent_svbf7_H3K27ac_bound,percent_svbf7_H3K27ac_unbound)

data_H3K27ac_rep = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac_rep,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

fig1 = plot_ly(data_H3K27ac_rep, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Rep condition) ", barmode = "stack")
fig1

fig1 = plot_ly(data_H3K27ac_rep, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig1 = fig1 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb ( Rep condition) ")
fig1

1.2.5.4 H3K27me3

1.2.5.4.1 Ctrl

	in H3K27me3	absent H3K27me3
in svbF7 unbound	2278	0
absent svbF7 unbound	12849	0

	in H3K27me3	absent H3K27me3
in svbF7 bound	33	0
absent svbF7 bound	15094	0

pvalue_fixe = phyper(33 - 1, 30, 15127 - 30, 15127)
pvalue_nonfixe = phyper(2278 - 1, 1800, 15127 - 1800, 15127)
presence = c(33,15094)
absence = c(0,0)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27me3_fixe)

presence = c(2278,15094)
absence = c(0,0)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 1 Celle du test de fisher pour les motifs non fixé est de 1`

total_svbF7_unbound_H3K27me3 = 2278 + 0
total_svbF7_bound_H3K27me3 = 33 + 0

percent_svbf7_H3K27me3_unbound = round(100 * 2278 / total_svbF7_unbound_H3K27me3, 2 ) 
percent_svbf7_unbound_without_H3K27me3 = round(100 * 0 / total_svbF7_unbound_H3K27me3, 2 ) 

percent_svbf7_H3K27me3_bound = round(100 * 33 / total_svbF7_bound_H3K27me3, 2 ) 
percent_svbf7_bound_without_H3K27me3 = round(100 * 0 / total_svbF7_bound_H3K27me3, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K27me3_absent = c(percent_svbf7_bound_without_H3K27me3,percent_svbf7_unbound_without_H3K27me3)

H3K27me3_present = c(percent_svbf7_H3K27me3_bound,percent_svbf7_H3K27me3_unbound)

data_H3K27me3_ctrl = data.frame(etat_svb,H3K27me3_absent,H3K27me3_present)
datatable(data_H3K27me3_ctrl,rownames = F,filter = "none",colnames = c("Regions without H3K27me3","Regions with H3K27me3" ))

fig2 = plot_ly(data_H3K27me3_ctrl, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb (Ctrl condition)", barmode = "stack")
fig2

fig2 = plot_ly(data_H3K27me3_ctrl, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb (Ctrl condition)")
fig2

1.2.5.4.2 Act

	in H3K27me3	absent H3K27me3
in svbF7 unbound	2192	0
absent svbF7 unbound	12117	0

	in H3K27me3	absent H3K27me3
in svbF7 bound	35	0
absent svbF7 bound	14274	0

pvalue_fixe = phyper(35 - 1, 30, 14309 - 30, 14309)
pvalue_nonfixe = phyper(2192 - 1, 1800, 14329 - 1800, 14309)
presence = c(35,14274)
absence = c(0,0)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27me3_fixe)

presence = c(2192,14274)
absence = c(0,0)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 1 Celle du test de fisher pour les motifs non fixé est de 1`

total_svbF7_unbound_H3K27me3 =  2192+ 0
total_svbF7_bound_H3K27me3 = 35 + 0

percent_svbf7_H3K27me3_unbound = round(100 * 2192 / total_svbF7_unbound_H3K27me3, 2 ) 
percent_svbf7_unbound_without_H3K27me3 = round(100 * 0 / total_svbF7_unbound_H3K27me3, 2 ) 

percent_svbf7_H3K27me3_bound = round(100 * 35 / total_svbF7_bound_H3K27me3, 2 ) 
percent_svbf7_bound_without_H3K27me3 = round(100 * 0 / total_svbF7_bound_H3K27me3, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K27me3_absent = c(percent_svbf7_bound_without_H3K27me3,percent_svbf7_unbound_without_H3K27me3)

H3K27me3_present = c(percent_svbf7_H3K27me3_bound,percent_svbf7_H3K27me3_unbound)

data_H3K27me3_act = data.frame(etat_svb,H3K27me3_absent,H3K27me3_present)
datatable(data_H3K27me3_act,rownames = F,filter = "none",colnames = c("Regions without H3K27me3","Regions with H3K27me3" ))

fig2 = plot_ly(data_H3K27me3_act, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb ( Act condition ) ", barmode = "stack")
fig2

fig2 = plot_ly(data_H3K27me3_act, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb ( Act condition ) ")
fig2

1.2.5.4.3 Rep

	in H3K27me3	absent H3K27me3
in svbF7 unbound	2265	0
absent svbF7 unbound	13036	0

	in H3K27me3	absent H3K27me3
in svbF7 bound	35	0
absent svbF7 bound	15266	0

pvalue_fixe = phyper(35 - 1, 30, 15301 - 30, 15301)
pvalue_nonfixe = phyper(2265 - 1, 1800, 15301 - 1800, 15301)
presence = c(35,15266)
absence = c(0,0)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27me3_fixe)

presence = c(2265,13036)
absence = c(0,0)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 1 Celle du test de fisher pour les motifs non fixé est de 1`

total_svbF7_unbound_H3K27me3 =  2265+ 0
total_svbF7_bound_H3K27me3 = 35 + 0

percent_svbf7_H3K27me3_unbound = round(100 * 2265 / total_svbF7_unbound_H3K27me3, 2 ) 
percent_svbf7_unbound_without_H3K27me3 = round(100 * 0 / total_svbF7_unbound_H3K27me3, 2 ) 

percent_svbf7_H3K27me3_bound = round(100 * 35 / total_svbF7_bound_H3K27me3, 2 ) 
percent_svbf7_bound_without_H3K27me3 = round(100 * 0 / total_svbF7_bound_H3K27me3, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K27me3_absent = c(percent_svbf7_bound_without_H3K27me3,percent_svbf7_unbound_without_H3K27me3)

H3K27me3_present = c(percent_svbf7_H3K27me3_bound,percent_svbf7_H3K27me3_unbound)

data_H3K27me3_rep = data.frame(etat_svb,H3K27me3_absent,H3K27me3_present)
datatable(data_H3K27me3_rep,rownames = F,filter = "none",colnames = c("Regions without H3K27me3","Regions with H3K27me3" ))

fig2 = plot_ly(data_H3K27me3_rep, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb ( Rep condition ) ", barmode = "stack")
fig2

fig2 = plot_ly(data_H3K27me3_rep, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color =I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent",color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb ( Rep condition ) ")
fig2

1.2.6 Comparaison entre les histones

data = merge(data_H3K4me1_ctrl,data_H3K4me1_act, by = "etat_svb" )
data = merge(data,data_H3K4me1_rep,by = "etat_svb")
data = merge(data,data_H3K4me3_ctrl, by = "etat_svb")
data = merge(data, data_H3K4me3_act, by = "etat_svb")
data = merge(data, data_H3K4me3_rep, by = "etat_svb")


data = merge(data, data_H3K27ac_ctrl, by = "etat_svb")
data = merge(data, data_H3K27ac_act, by = "etat_svb")
data = merge(data, data_H3K27ac_rep, by = "etat_svb")
data = merge(data, data_H3K27me3_ctrl, by = "etat_svb")
data = merge(data, data_H3K27me3_act, by = "etat_svb")
data = merge(data, data_H3K27me3_rep, by = "etat_svb")

1.2.7 Embryon

1.2.7.1 H3K27me3

1.2.7.1.1 8-12h

	in H3K27me3	absent H3K27me3
in svbF7 unbound	695	428
absent svbF7 unbound	6067	12192

	in H3K27me3	absent H3K27me3
in svbF7 bound	504	285
absent svbF7 bound	6258	11859

pvalue_fixe = phyper(504 - 1, 789, 18906 - 789, 6762)
pvalue_nonfixe = phyper(695 - 1, 1123, 19382 - 1123, 6762)
presence = c(504,6258)
absence = c(285,11859)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27me3_fixe)

presence = c(695,6067)
absence = c(428,12192)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 3.027742410^{-60} Celle du test de fisher pour les motifs non fixé est de 3.396497310^{-80}`

total_svbF7_unbound_H3K27me3 = 695 + 428
total_svbF7_bound_H3K27me3 = 504 + 285

percent_svbf7_H3K27me3_unbound = round(100 * 695 / total_svbF7_unbound_H3K27me3, 2 ) 
percent_svbf7_unbound_without_H3K27me3 = round(100 * 428 / total_svbF7_unbound_H3K27me3, 2 ) 

percent_svbf7_H3K27me3_bound = round(100 * 504 / total_svbF7_bound_H3K27me3, 2 ) 
percent_svbf7_bound_without_H3K27me3 = round(100 * 285 / total_svbF7_bound_H3K27me3, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K27me3_absent = c(percent_svbf7_bound_without_H3K27me3,percent_svbf7_unbound_without_H3K27me3)

H3K27me3_present = c(percent_svbf7_H3K27me3_bound,percent_svbf7_H3K27me3_unbound)

data_H3K27me3 = data.frame(etat_svb,H3K27me3_absent,H3K27me3_present)
datatable(data_H3K27me3,rownames = F,filter = "none",colnames = c("Regions without H3K27me3","Regions with H3K27me3" ))

fig2 = plot_ly(data_H3K27me3, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color = I("orange"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent", color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 ( embryo 8-12h) enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig2

fig2 = plot_ly(data_H3K27me3, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color = I("orange"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent", color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 ( embryo 8-12h) enrichment on svbF7 bound or not by Svb")
fig2

1.2.7.1.2 12-16h

	in H3K27me3	absent H3K27me3
in svbF7 unbound	689	434
absent svbF7 unbound	5917	12139

	in H3K27me3	absent H3K27me3
in svbF7 bound	487	302
absent svbF7 bound	6119	11804

pvalue_fixe = phyper(487 - 1, 789, 18712 - 789, 6606)
pvalue_nonfixe = phyper(689 - 1, 1123, 19179 - 1123, 6606)
presence = c(487,6119)
absence = c(302,11804)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27me3_fixe)

presence = c(689,5917)
absence = c(434,12139)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 1.816562810^{-53} Celle du test de fisher pour les motifs non fixé est de 4.020798110^{-80}`

total_svbF7_unbound_H3K27me3 = 689 + 434
total_svbF7_bound_H3K27me3 = 487 + 302

percent_svbf7_H3K27me3_unbound = round(100 * 689 / total_svbF7_unbound_H3K27me3, 2 ) 
percent_svbf7_unbound_without_H3K27me3 = round(100 * 434 / total_svbF7_unbound_H3K27me3, 2 ) 

percent_svbf7_H3K27me3_bound = round(100 * 487 / total_svbF7_bound_H3K27me3, 2 ) 
percent_svbf7_bound_without_H3K27me3 = round(100 * 302 / total_svbF7_bound_H3K27me3, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K27me3_absent = c(percent_svbf7_bound_without_H3K27me3,percent_svbf7_unbound_without_H3K27me3)

H3K27me3_present = c(percent_svbf7_H3K27me3_bound,percent_svbf7_H3K27me3_unbound)

data_H3K27me3 = data.frame(etat_svb,H3K27me3_absent,H3K27me3_present)
datatable(data_H3K27me3,rownames = F,filter = "none",colnames = c("Regions without H3K27me3","Regions with H3K27me3" ))

fig2 = plot_ly(data_H3K27me3, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color = I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent", color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig2

fig2 = plot_ly(data_H3K27me3, x = ~etat_svb,y = ~H3K27me3_present, type = "bar",name = "H3K27me3 present", color = I("purple"))
fig2 = fig2 %>% add_trace(y = ~H3K27me3_absent, name = "H3K27me3 absent", color = I("grey"))
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb")
fig2

1.2.7.2 H3K4me1

1.2.7.2.1 8-12h

	in H3K4me1	absent H3K4me1
in svbF7 unbound	933	190
absent svbF7 unbound	13651	5808

	in H3K4me1	absent H3K4me1
in svbF7 bound	1120	0
absent svbF7 bound	13470	5456

pvalue_fixe = phyper(1120 - 1, 789, 20046 - 789, 14590)
pvalue_nonfixe = phyper(933 - 1, 1123, 20582 - 1123, 14584)
presence = c(1120,13470)
absence = c(0,5456)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me1_fixe_8_12 = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me1_fixe_8_12)

presence = c(933,13651)
absence = c(190,5808)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me1_nonfixe_8_12 = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me1_nonfixe_8_12)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 1.825385410^{-160} Celle du test de fisher pour les motifs non fixé est de 2.344941810^{-22}`

total_svbF7_unbound_H3K4me1 = 933 + 190
total_svbF7_bound_H3K4me1 = 1120 + 0

percent_svbf7_H3K4me1_unbound = round(100 * 933 / total_svbF7_unbound_H3K4me1, 2 ) 
percent_svbf7_unbound_without_H3K4me1 = round(100 * 190 / total_svbF7_unbound_H3K4me1, 2 ) 

percent_svbf7_H3K4me1_bound = round(100 * 1120 / total_svbF7_bound_H3K4me1, 2 ) 
percent_svbf7_bound_without_H3K4me1 = round(100 * 0 / total_svbF7_bound_H3K4me1, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me1_absent = c(percent_svbf7_bound_without_H3K4me1,percent_svbf7_unbound_without_H3K4me1)

H3K4me1_present = c(percent_svbf7_H3K4me1_bound,percent_svbf7_H3K4me1_unbound)

data_H3K4me1 = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1,rownames = F,filter = "none",colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig4 = plot_ly(data_H3K4me1, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig4 = fig4 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me1 (embryo 8-12h) enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig4

fig4 = plot_ly(data_H3K4me1, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig4 = fig4 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me1 (embryo 8-12h) enrichment on svbF7 bound or not by Svb")
fig4

1.2.7.2.2 12-16h

	in H3K4me1	absent H3K4me1
in svbF7 unbound	725	398
absent svbF7 unbound	11878	7878

	in H3K4me1	absent H3K4me1
in svbF7 bound	1012	0
absent svbF7 bound	11597	7718

pvalue_fixe = phyper(1012 - 1, 789, 20327 - 789, 12603)
pvalue_nonfixe = phyper(725 - 1, 1123, 20879 - 1123, 12603)
presence = c(1012,11597)
absence = c(0,7718)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me1_fixe_12_16 = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me1_fixe_12_16)

presence = c(725,11878)
absence = c(398,7878)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me1_nonfixe_12_16 = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me1_nonfixe_12_16)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 0.9983645

La pvalue du test de fisher pour les motifs fixé est de 1.6056910^{-217} Celle du test de fisher pour les motifs non fixé est de 0.0031876`

total_svbF7_unbound_H3K4me1 = 725 + 398
total_svbF7_bound_H3K4me1 = 1012 + 0

percent_svbf7_H3K4me1_unbound = round(100 * 725 / total_svbF7_unbound_H3K4me1, 2 ) 
percent_svbf7_unbound_without_H3K4me1 = round(100 * 398 / total_svbF7_unbound_H3K4me1, 2 ) 

percent_svbf7_H3K4me1_bound = round(100 * 1012 / total_svbF7_bound_H3K4me1, 2 ) 
percent_svbf7_bound_without_H3K4me1 = round(100 * 0 / total_svbF7_bound_H3K4me1, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me1_absent = c(percent_svbf7_bound_without_H3K4me1,percent_svbf7_unbound_without_H3K4me1)

H3K4me1_present = c(percent_svbf7_H3K4me1_bound,percent_svbf7_H3K4me1_unbound)

data_H3K4me1 = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1,rownames = F,filter = "none",colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig3 = plot_ly(data_H3K4me1, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 (embryo 12h-16h) enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig3

fig3 = plot_ly(data_H3K4me1, x = ~etat_svb,y = ~H3K4me1_present, type = "bar",name = "H3K4me1 present", color = I("orange"))
fig3 = fig3 %>% add_trace(y = ~H3K4me1_absent, name = "H3K4me1 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 (embryo 12h-16h) enrichment on svbF7 bound or not by Svb")
fig3

1.2.7.3 H3K4me3

1.2.7.3.1 8-12h

	in H3K4me3	absent H3K4me3
in svbF7 unbound	296	827
absent svbF7 unbound	5876	12263

	in H3K4me3	absent H3K4me3
in svbF7 bound	299	490
absent svbF7 bound	5873	12129

pvalue_fixe = phyper(299 - 1, 789, 18791 - 789, 6172)
pvalue_nonfixe = phyper(296 - 1, 789, 19262 - 1123, 6172)
presence = c(299,5873)
absence = c(490,12129)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me3_fixe_8_12 = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me3_fixe_8_12)

presence = c(296,586)
absence = c(398,7878)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me3_nonfixe_8_12 = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me3_nonfixe_8_12)

La pvalue du test de hypergéométrique pour les motifs fixé est de 0.9987244 Celle du test de hypergéométrique pour les motifs non fixé est de 0.9983399

La pvalue du test de fisher pour les motifs fixé est de 0.0022141 Celle du test de fisher pour les motifs non fixé est de 8.395345710^{-132}`

total_svbF7_unbound_H3K4me3 = 296 + 827
total_svbF7_bound_H3K4me3 = 299 + 490

percent_svbf7_H3K4me3_unbound = round(100 * 296 / total_svbF7_unbound_H3K4me3, 2 ) 
percent_svbf7_unbound_without_H3K4me3 = round(100 * 827 / total_svbF7_unbound_H3K4me3, 2 ) 

percent_svbf7_H3K4me3_bound = round(100 * 299 / total_svbF7_bound_H3K4me3, 2 ) 
percent_svbf7_bound_without_H3K4me3 = round(100 * 490 / total_svbF7_bound_H3K4me3, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me3_absent = c(percent_svbf7_bound_without_H3K4me3,percent_svbf7_unbound_without_H3K4me3)

H3K4me3_present = c(percent_svbf7_H3K4me3_bound,percent_svbf7_H3K4me3_unbound)

data_H3K4me3 = data.frame(etat_svb,H3K4me3_absent,H3K4me3_present)
datatable(data_H3K4me3,rownames = F,filter = "none",colnames = c("Regions without H3K4me3","Regions with H3K4me3" ))

fig3 = plot_ly(data_H3K4me3, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present" , color = I("pink"))
fig3 = fig3 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me3 (embryo 8-12h) enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig3

fig3 = plot_ly(data_H3K4me3, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present" , color = I("pink"))
fig3 = fig3 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me3 (embryo 8-12h) enrichment on svbF7 bound or not by Svb")
fig3

1.2.7.3.1.1 Rapport K4me1/ K4me3

total_K4me1_8_12 = 14595
total_K4me3_8_12 = 6179
rapp_fixe_8_12 = round((contingence_H3K4me1_fixe_8_12[1,1]/total_K4me1_8_12) / (contingence_H3K4me3_fixe_8_12[1,1]/total_K4me3_8_12),2)
rapp_nonfixe_8_12 = round((contingence_H3K4me1_nonfixe[1,1]/total_K4me1_8_12) / (contingence_H3K4me3_nonfixe[1,1]/total_K4me3_8_12),2)

Il y a 1.59 fois de H3K4me1 que de H3K4me3 fixé.

Il y a 1.8 fois de H3K4me1 que de H3K4me3 non fixé.

1.2.7.3.2 12-16h

	in H3K4me3	absent H3K4me3
in svbF7 unbound	299	824
absent svbF7 unbound	6467	12309

	in H3K4me3	absent H3K4me3
in svbF7 bound	340	449
absent svbF7 bound	6427	12182

pvalue_fixe = phyper(340 - 1, 789, 19398 - 789, 6767)
pvalue_nonfixe = phyper(299 - 1, 1123, 19899 - 1123, 6766)
presence = c(340,6427)
absence = c(449,12182)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me3_fixe_12_16 = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me3_fixe_12_16)

presence = c(299,6467)
absence = c(824,12309)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me3_nonfixe_12_16 = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me3_nonfixe_12_16)

La pvalue du test de hypergéométrique pour les motifs fixé est de 0.9999993 Celle du test de hypergéométrique pour les motifs non fixé est de 1.828087910^{-8}

La pvalue du test de fisher pour les motifs fixé est de 1.210093210^{-6} Celle du test de fisher pour les motifs non fixé est de 4.834952910^{-8}`

total_svbF7_unbound_H3K4me3 = 299 + 824
total_svbF7_bound_H3K4me3 = 340 + 449

percent_svbf7_H3K4me3_unbound = round(100 * 299 / total_svbF7_unbound_H3K4me3, 2 ) 
percent_svbf7_unbound_without_H3K4me3 = round(100 * 824 / total_svbF7_unbound_H3K4me3, 2 ) 

percent_svbf7_H3K4me3_bound = round(100 * 340 / total_svbF7_bound_H3K4me3, 2 ) 
percent_svbf7_bound_without_H3K4me3 = round(100 * 449 / total_svbF7_bound_H3K4me3, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K4me3_absent = c(percent_svbf7_bound_without_H3K4me3,percent_svbf7_unbound_without_H3K4me3)

H3K4me3_present = c(percent_svbf7_H3K4me3_bound,percent_svbf7_H3K4me3_unbound)

data_H3K4me3 = data.frame(etat_svb,H3K4me3_absent,H3K4me3_present)
datatable(data_H3K4me3,rownames = F,filter = "none",colnames = c("Regions without H3K4me3","Regions with H3K4me3" ))

fig3 = plot_ly(data_H3K4me3, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig3 = fig3 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me3 (embryo 12h-16h) enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig3

fig3 = plot_ly(data_H3K4me3, x = ~etat_svb,y = ~H3K4me3_present, type = "bar",name = "H3K4me3 present", color = I("pink"))
fig3 = fig3 %>% add_trace(y = ~H3K4me3_absent, name = "H3K4me3 absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me3 (embryo 12h-16h) enrichment on svbF7 bound or not by Svb")
fig3

1.2.7.3.2.1 Rapport K4me1/ K4me3

total_K4me1_12_16 = 12613
total_K4me3_12_16 = 6775
rapp_fixe_12_16 = round((contingence_H3K4me1_fixe_8_12[1,1]/total_K4me1_12_16) / (contingence_H3K4me3_fixe_8_12[1,1]/total_K4me3_12_16),2)
rapp_nonfixe_12_16 = round((contingence_H3K4me1_nonfixe[1,1]/total_K4me1_12_16) / (contingence_H3K4me3_nonfixe[1,1]/total_K4me3_12_16),2)

Il y a 1.59 fois de H3K4me1 que de H3K4me3 fixé.

Il y a 1.8 fois de H3K4me1 que de H3K4me3 non fixé.

1.2.7.4 H3K27ac

1.2.7.4.1 8-12h

	in H3K27ac	absent H3K27ac
in svbF7 unbound	515	608
absent svbF7 unbound	9616	9778

	in H3K27ac	absent H3K27ac
in svbF7 bound	665	124
absent svbF7 bound	9467	9727

pvalue_fixe = phyper(665 - 1, 789, 19983 - 789, 10132)
pvalue_nonfixe = phyper(515 - 1, 1123, 20517 - 1123, 10131)
presence = c(665,9467)
absence = c(124,9727)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27ac_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27ac_fixe)

presence = c(515,9616)
absence = c(608,9778)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27ac_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 0.0069799

La pvalue du test de fisher pour les motifs fixé est de 3.375421710^{-90} Celle du test de fisher pour les motifs non fixé est de 0.0153226`

total_svbF7_unbound_H3K27ac = 515 + 608
total_svbF7_bound_H3K27ac = 665 + 124

percent_svbf7_H3K27ac_unbound = round(100 * 515 / total_svbF7_unbound_H3K27ac, 2 ) 
percent_svbf7_unbound_without_H3K27ac = round(100 * 608 / total_svbF7_unbound_H3K27ac, 2 ) 

percent_svbf7_H3K27ac_bound = round(100 * 665 / total_svbF7_bound_H3K27ac, 2 ) 
percent_svbf7_bound_without_H3K27ac = round(100 * 124 / total_svbF7_bound_H3K27ac, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)

H3K27ac_present = c(percent_svbf7_H3K27ac_bound,percent_svbf7_H3K27ac_unbound)

data_H3K27ac = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

fig3 = plot_ly(data_H3K27ac, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig3 = fig3 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K27ac (embryo 8-12h) enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig3

fig3 = plot_ly(data_H3K27ac, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig3 = fig3 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K27ac (embryo 8-12h) enrichment on svbF7 bound or not by Svb")
fig3

1.2.7.4.2 12-16h

	in H3K27ac	absent H3K27ac
in svbF7 unbound	500	623
absent svbF7 unbound	9959	9790

	in H3K27ac	absent H3K27ac
in svbF7 bound	783	6
absent svbF7 bound	9676	9856

pvalue_fixe = phyper(783 - 1, 789, 20321 - 789, 10459)
pvalue_nonfixe = phyper(500 - 1, 1123, 20872 - 1123, 10459)
presence = c(665,9467)
absence = c(124,9727)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27ac_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27ac_fixe)

presence = c(515,9616)
absence = c(608,9778)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27ac_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 5.152453810^{-5}

La pvalue du test de fisher pour les motifs fixé est de 3.375421710^{-90} Celle du test de fisher pour les motifs non fixé est de 0.0153226`

total_svbF7_unbound_H3K27ac = 500 + 623
total_svbF7_bound_H3K27ac = 783 + 6

percent_svbf7_H3K27ac_unbound = round(100 * 500 / total_svbF7_unbound_H3K27ac, 2 ) 
percent_svbf7_unbound_without_H3K27ac = round(100 * 623 / total_svbF7_unbound_H3K27ac, 2 ) 

percent_svbf7_H3K27ac_bound = round(100 * 783 / total_svbF7_bound_H3K27ac, 2 ) 
percent_svbf7_bound_without_H3K27ac = round(100 * 6 / total_svbF7_bound_H3K27ac, 2 ) 


etat_svb = c("svbF7_bound","svbF7_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)

H3K27ac_present = c(percent_svbf7_H3K27ac_bound,percent_svbf7_H3K27ac_unbound)

data_H3K27ac = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

fig3 = plot_ly(data_H3K27ac, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig3 = fig3 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K27ac (embryo 8-12h) enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig3

data_H3K27ac = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

fig3 = plot_ly(data_H3K27ac, x = ~etat_svb,y = ~H3K27ac_present, type = "bar",name = "H3K27ac present", color = I("blue"))
fig3 = fig3 %>% add_trace(y = ~H3K27ac_absent, name = "H3K27ac absent", color = I("grey"))
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K27ac (embryo 8-12h) enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig3

2 En Flankant les motifs de 300pb (taille des pics de Svb )

2.1 Determination des motifs ayant de la fixation ou non

2.1.1 Stringent

motif_svb_Bind_overlap_300 = findOverlapsOfPeaks(B1.peaks, ovoQ6_droso_gr_unique_300,svbF7_cons_gr_300)
makeVennDiagram(motif_svb_Bind_overlap_300,totalTest = 100,NameOfPeaks = c("SvbAct","ovoQ6", "svbF7"),fill=c("green","purple","orange"))

## $p.value
##      SvbAct ovoQ6 svbF7 pval
## [1,]      0     1     1  NaN
## [2,]      1     0     1  NaN
## [3,]      1     1     0  NaN
## 
## $vennCounts
##      SvbAct ovoQ6 svbF7 Counts count.B1.peaks count.ovoQ6_droso_gr_unique_300
## [1,]      0     0     0 -64227              0                               0
## [2,]      0     0     1    358              0                               0
## [3,]      0     1     0  56902              0                           56902
## [4,]      0     1     1   1645              0                            2869
## [5,]      1     0     0   2179           2179                               0
## [6,]      1     0     1     42             45                               0
## [7,]      1     1     0   2919           2971                            5012
## [8,]      1     1     1    282            317                             740
##      count.svbF7_cons_gr_300
## [1,]                       0
## [2,]                     358
## [3,]                       0
## [4,]                    1650
## [5,]                       0
## [6,]                      45
## [7,]                       0
## [8,]                     301
## attr(,"class")
## [1] "VennCounts"

ovoQ6_droso_svb_bind_overlap_300 = findOverlapsOfPeaks(B1.peaks,ovoQ6_droso_gr_unique_300)
makeVennDiagram(ovoQ6_droso_svb_bind_overlap_300,totalTest = 100,NameOfPeaks = c("SvbAct","ovoQ6"),fill=c("green","purple"))

## $p.value
##      SvbAct ovoQ6 pval
## [1,]      1     1  NaN
## 
## $vennCounts
##      SvbAct ovoQ6 Counts count.B1.peaks count.ovoQ6_droso_gr_unique_300
## [1,]      0     0 -65207              0                               0
## [2,]      0     1  59851              0                           59851
## [3,]      1     0   2227           2227                               0
## [4,]      1     1   3229           3285                            5672
## attr(,"class")
## [1] "VennCounts"

svbF7_cons_gr_svb_bind_overlap_300 = findOverlapsOfPeaks(B1.peaks,svbF7_cons_gr_300)
makeVennDiagram(svbF7_cons_gr_svb_bind_overlap_300,totalTest = 100,NameOfPeaks = c("SvbAct", "svbF7"),fill=c("green","orange"))

## $p.value
##      SvbAct svbF7 pval
## [1,]      1     1  NaN
## 
## $vennCounts
##      SvbAct svbF7 Counts count.B1.peaks count.svbF7_cons_gr_300
## [1,]      0     0  -7422              0                       0
## [2,]      0     1   2029              0                    2029
## [3,]      1     0   5187           5187                       0
## [4,]      1     1    306            325                     325
## attr(,"class")
## [1] "VennCounts"

svbF7_motifs_unbound_300 = as.data.frame(svbF7_cons_gr_svb_bind_overlap_300$peaklist$svbF7_cons_gr_300)

svbF7_motifs_bound_300 = as.data.frame(svbF7_cons_gr_svb_bind_overlap_300$peaklist$`B1.peaks///svbF7_cons_gr_300`)

ovoQ6_motifs_unbound_300 = as.data.frame(ovoQ6_droso_svb_bind_overlap_300$peaklist$ovoQ6_droso_gr_unique_300)

ovoQ6_motifs_bound_300 = as.data.frame(ovoQ6_droso_svb_bind_overlap_300$peaklist$`B1.peaks///ovoQ6_droso_gr_unique_300`)

2.1.2 Sensible

motif_svb_Bind_overlap_300_low = findOverlapsOfPeaks(SvbACT.peaks_low, ovoQ6_droso_gr_unique_300,svbF7_cons_gr_300)
makeVennDiagram(motif_svb_Bind_overlap_300_low,totalTest = 100,NameOfPeaks = c("SvbAct","ovoQ6", "svbF7"),fill=c("green","purple","orange"))

## $p.value
##      SvbAct ovoQ6 svbF7 pval
## [1,]      0     1     1  NaN
## [2,]      1     0     1  NaN
## [3,]      1     1     0  NaN
## 
## $vennCounts
##      SvbAct ovoQ6 svbF7 Counts count.SvbACT.peaks_low
## [1,]      0     0     0 -64446                      0
## [2,]      0     0     1    351                      0
## [3,]      0     1     0  55764                      0
## [4,]      0     1     1   1603                      0
## [5,]      1     0     0   2816                   2816
## [6,]      1     0     1     49                     51
## [7,]      1     1     0   3639                   3703
## [8,]      1     1     1    324                    368
##      count.ovoQ6_droso_gr_unique_300 count.svbF7_cons_gr_300
## [1,]                               0                       0
## [2,]                               0                     351
## [3,]                           55764                       0
## [4,]                            2781                    1608
## [5,]                               0                       0
## [6,]                               0                      52
## [7,]                            6133                       0
## [8,]                             845                     343
## attr(,"class")
## [1] "VennCounts"

ovoQ6_droso_svb_bind_overlap_300_low = findOverlapsOfPeaks(SvbACT.peaks_low,ovoQ6_droso_gr_unique_300)
makeVennDiagram(ovoQ6_droso_svb_bind_overlap_300_low,totalTest = 100,NameOfPeaks = c("SvbAct","ovoQ6"),fill=c("green","purple"))

## $p.value
##      SvbAct ovoQ6 pval
## [1,]      1     1  NaN
## 
## $vennCounts
##      SvbAct ovoQ6 Counts count.SvbACT.peaks_low count.ovoQ6_droso_gr_unique_300
## [1,]      0     0 -65396                      0                               0
## [2,]      0     1  58624                      0                           58624
## [3,]      1     0   2870                   2870                               0
## [4,]      1     1   4002                   4068                            6899
## attr(,"class")
## [1] "VennCounts"

svbF7_cons_gr_svb_bind_overlap_300_low = findOverlapsOfPeaks(SvbACT.peaks_low,svbF7_cons_gr_300)
makeVennDiagram(svbF7_cons_gr_svb_bind_overlap_300_low,totalTest = 100,NameOfPeaks = c("SvbAct", "svbF7"),fill=c("green","orange"))

## $p.value
##      SvbAct svbF7 pval
## [1,]      1     1  NaN
## 
## $vennCounts
##      SvbAct svbF7 Counts count.SvbACT.peaks_low count.svbF7_cons_gr_300
## [1,]      0     0  -8808                      0                       0
## [2,]      0     1   1986                      0                    1986
## [3,]      1     0   6573                   6573                       0
## [4,]      1     1    349                    365                     368
## attr(,"class")
## [1] "VennCounts"

svbF7_motifs_unbound_300_low = as.data.frame(svbF7_cons_gr_svb_bind_overlap_300_low$peaklist$svbF7_cons_gr_300)

svbF7_motifs_bound_300_low = as.data.frame(svbF7_cons_gr_svb_bind_overlap_300_low$peaklist$`SvbACT.peaks_low///svbF7_cons_gr_300`)

ovoQ6_motifs_unbound_300_low = as.data.frame(ovoQ6_droso_svb_bind_overlap_300_low$peaklist$ovoQ6_droso_gr_unique_300)

ovoQ6_motifs_bound_300_low = as.data.frame(ovoQ6_droso_svb_bind_overlap_300_low$peaklist$`SvbACT.peaks_low///ovoQ6_droso_gr_unique_300`)

2.1.3 Embryon

motif_svb_Bind_overlap_300_embryo = findOverlapsOfPeaks(SvbACT.peaks_embryo, ovoQ6_droso_gr_unique_300,svbF7_cons_gr_300)
makeVennDiagram(motif_svb_Bind_overlap_300_embryo,totalTest = 100,NameOfPeaks = c("SvbAct","ovoQ6", "svbF7"),fill=c("green","purple","orange"))

## $p.value
##      SvbAct ovoQ6 svbF7 pval
## [1,]      0     1     1  NaN
## [2,]      1     0     1  NaN
## [3,]      1     1     0  NaN
## 
## $vennCounts
##      SvbAct ovoQ6 svbF7 Counts count.SvbACT.peaks_embryo
## [1,]      0     0     0 -66627                         0
## [2,]      0     0     1    348                         0
## [3,]      0     1     0  56218                         0
## [4,]      0     1     1   1568                         0
## [5,]      1     0     0   4392                      4392
## [6,]      1     0     1     50                        53
## [7,]      1     1     0   3796                      3858
## [8,]      1     1     1    355                       403
##      count.ovoQ6_droso_gr_unique_300 count.svbF7_cons_gr_300
## [1,]                               0                       0
## [2,]                               0                     348
## [3,]                           56218                       0
## [4,]                            2734                    1572
## [5,]                               0                       0
## [6,]                               0                      52
## [7,]                            5686                       0
## [8,]                             885                     382
## attr(,"class")
## [1] "VennCounts"

ovoQ6_droso_svb_bind_overlap_300_embryo = findOverlapsOfPeaks(SvbACT.peaks_embryo,ovoQ6_droso_gr_unique_300)
makeVennDiagram(ovoQ6_droso_svb_bind_overlap_300_embryo,totalTest = 100,NameOfPeaks = c("SvbAct","ovoQ6"),fill=c("green","purple"))

## $p.value
##      SvbAct ovoQ6 pval
## [1,]      1     1  NaN
## 
## $vennCounts
##      SvbAct ovoQ6 Counts count.SvbACT.peaks_embryo
## [1,]      0     0 -67606                         0
## [2,]      0     1  59071                         0
## [3,]      1     0   4446                      4446
## [4,]      1     1   4189                      4260
##      count.ovoQ6_droso_gr_unique_300
## [1,]                               0
## [2,]                           59071
## [3,]                               0
## [4,]                            6452
## attr(,"class")
## [1] "VennCounts"

svbF7_cons_gr_svb_bind_overlap_300_embryo = findOverlapsOfPeaks(SvbACT.peaks_embryo,svbF7_cons_gr_300)
makeVennDiagram(svbF7_cons_gr_svb_bind_overlap_300_embryo,totalTest = 100,NameOfPeaks = c("SvbAct", "svbF7"),fill=c("green","orange"))

## $p.value
##      SvbAct svbF7 pval
## [1,]      1     1  NaN
## 
## $vennCounts
##      SvbAct svbF7 Counts count.SvbACT.peaks_embryo count.svbF7_cons_gr_300
## [1,]      0     0 -10537                         0                       0
## [2,]      0     1   1959                         0                    1959
## [3,]      1     0   8307                      8307                       0
## [4,]      1     1    371                       399                     395
## attr(,"class")
## [1] "VennCounts"

svbF7_motifs_unbound_300_embryo = as.data.frame(svbF7_cons_gr_svb_bind_overlap_300_embryo$peaklist$svbF7_cons_gr_300)

svbF7_motifs_bound_300_embryo = as.data.frame(svbF7_cons_gr_svb_bind_overlap_300_embryo$peaklist$`SvbACT.peaks_embryo///svbF7_cons_gr_300`)

ovoQ6_motifs_unbound_300_embryo = as.data.frame(ovoQ6_droso_svb_bind_overlap_300_embryo$peaklist$ovoQ6_droso_gr_unique_300)

ovoQ6_motifs_bound_300_embryo = as.data.frame(ovoQ6_droso_svb_bind_overlap_300_embryo$peaklist$`SvbACT.peaks_embryo///ovoQ6_droso_gr_unique_300`)

2.2 Comparaison avec les marques d’histones

2.2.1 Stringent

2.2.1.1 H3K27ac

	in H3K27ac	absent H3K27ac
in svbF7 unbound	167	1764
absent svbF7 unbound	4981	24285

	in H3K27ac	absent H3K27ac
in svbF7 bound	174	127
absent svbF7 bound	4979	25028

pvalue_fixe = phyper(174 - 1, 301, 30308 - 301, 5153) 
pvalue_nonfixe = phyper(167 - 1, 1931, 31197 - 1931, 5148)
presence = c(174,4979)
absence = c(127,25028)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27ac_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27ac_fixe)

presence = c(167,4981)
absence = c(1764,24285)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27ac_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1.632559610^{-25}

La pvalue du test de fisher pour les motifs fixé est de 6.336471410^{-58} Celle du test de fisher pour les motifs non fixé est de 7.33482810^{-25}`

total_svbF7_unbound_H3K27ac = 117 + 1773
total_svbF7_bound_H3K27ac = 214 + 128

percent_svbf7_H3K27ac_unbound = round(100 * 117 / total_svbF7_unbound_H3K27ac, 2 ) 
percent_svbf7_unbound_without_H3K27ac = round(100 * 1773 / total_svbF7_unbound_H3K27ac, 2 ) 

percent_svbf7_H3K27ac_bound = round(100 * 214 / total_svbF7_bound_H3K27ac, 2 ) 
percent_svbf7_bound_without_H3K27ac = round(100 * 128 / total_svbF7_bound_H3K27ac, 2 ) 



etat_svb = c("svbF7_bound","svb_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)

H3K27ac_present = c(percent_svbf7_H3K27ac_bound,percent_svbf7_H3K27ac_unbound)

data_H3K27ac = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

fig1 = plot_ly(data_H3K27ac, x = ~etat_svb,y = ~H3K27ac_absent, type = "bar",name = "H3K27ac absent")
fig1 = fig1 %>% add_trace(y = ~H3K27ac_present, name = "H3K27ac present")
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb")
fig1

fig1 = plot_ly(data_H3K27ac, x = ~etat_svb,y = ~H3K27ac_absent, type = "bar",name = "H3K27ac absent")
fig1 = fig1 %>% add_trace(y = ~H3K27ac_present, name = "H3K27ac present")
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig1

2.2.1.2 H3K27me3

	in H3K27me3	absent H3K27me3
in svbF7 unbound	736	1195
absent svbF7 unbound	12699	49770

	in H3K27me3	absent H3K27me3
in svbF7 bound	48	253
absent svbF7 bound	13390	46987

pvalue_fixe = phyper(48 - 1, 301, 60678 - 301, 13438)
pvalue_nonfixe =phyper(736 - 1, 1931, 64400 - 1931, 13435)
presence = c(48,13390)
absence = c(253,46987)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27me3_fixe)

presence = c(736,12699)
absence = c(1195,49770)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 0.0029026 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 0.0081554 Celle du test de fisher pour les motifs non fixé est de 7.33482810^{-25}`

total_svbF7_unbound_H3K27me3 = 736 + 1195
total_svbF7_bound_H3K27me3 = 48 + 253

percent_svbf7_H3K27me3_unbound = round(100 * 736 / total_svbF7_unbound_H3K27me3, 2 ) 
percent_svbf7_unbound_without_H3K27me3 = round(100 * 1195 / total_svbF7_unbound_H3K27me3, 2 ) 

percent_svbf7_H3K27me3_bound = round(100 * 48 / total_svbF7_bound_H3K27me3, 2 ) 
percent_svbf7_bound_without_H3K27me3 = round(100 * 253 / total_svbF7_bound_H3K27me3, 2 ) 


etat_svb = c("svbF7_bound","svb_unbound")
H3K27me3_absent = c(percent_svbf7_bound_without_H3K27me3,percent_svbf7_unbound_without_H3K27me3)

H3K27me3_present = c(percent_svbf7_H3K27me3_bound,percent_svbf7_H3K27me3_unbound)

data_H3K27me3 = data.frame(etat_svb,H3K27me3_absent,H3K27me3_present)
datatable(data_H3K27me3,rownames = F,filter = "none",colnames = c("Regions without H3K27me3","Regions with H3K27me3" ))

fig2 = plot_ly(data_H3K27me3, x = ~etat_svb,y = ~H3K27me3_absent, type = "bar",name = "H3K27me3 absent")
fig2 = fig2 %>% add_trace(y = ~H3K27me3_present, name = "H3K27me3 present")
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb")
fig2

fig2 = plot_ly(data_H3K27me3, x = ~etat_svb,y = ~H3K27me3_absent, type = "bar",name = "H3K27me3 absent")
fig2 = fig2 %>% add_trace(y = ~H3K27me3_present, name = "H3K27me3 present")
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig2

2.2.1.3 H3K4me1

	in H3K4me1	absent H3K4me1
in svbF7 unbound	321	1610
absent svbF7 unbound	8445	31802

	in H3K4me1	absent H3K4me1
in svbF7 bound	211	90
absent svbF7 bound	8561	31674

pvalue_fixe = phyper(321 - 1, 1931, 42178 - 1931, 8766)
pvalue_nonfixe = phyper(211 - 1, 301, 40536 - 301, 8772)
presence = c(211,8561)
absence = c(90,31674)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me1_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me1_fixe)

presence = c(736,12699)
absence = c(1195,49770)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me1_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me1_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 9.840557510^{-7} Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 1.068099710^{-72} Celle du test de fisher pour les motifs non fixé est de 1.97416510^{-69}`

total_svbF7_unbound_H3K4me1 = 321 + 1610
total_svbF7_bound_H3K4me1 = 211 + 90

percent_svbf7_H3K4me1_unbound = round(100 * 321 / total_svbF7_unbound_H3K4me1, 2 ) 
percent_svbf7_unbound_without_H3K4me1 = round(100 * 1610  / total_svbF7_unbound_H3K4me1, 2 ) 

percent_svbf7_H3K4me1_bound = round(100 * 211 / total_svbF7_bound_H3K4me1, 2 ) 
percent_svbf7_bound_without_H3K4me1 = round(100 * 90 / total_svbF7_bound_H3K4me1, 2 ) 

etat_svb = c("svbF7_bound","svb_unbound")
H3K4me1_absent = c(percent_svbf7_bound_without_H3K4me1,percent_svbf7_unbound_without_H3K4me1)

H3K4me1_present = c(percent_svbf7_H3K4me1_bound,percent_svbf7_H3K4me1_unbound)

data_H3K4me1 = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1,rownames = F,filter = "none",colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig3 = plot_ly(data_H3K4me1, x = ~etat_svb,y = ~H3K4me1_absent, type = "bar",name = "H3K4me1 absent")
fig3 = fig3 %>% add_trace(y = ~H3K4me1_present, name = "H3K4me1 present")
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb")
fig3

fig3 = plot_ly(data_H3K4me1, x = ~etat_svb,y = ~H3K4me1_absent, type = "bar",name = "H3K4me1 absent")
fig3 = fig3 %>% add_trace(y = ~H3K4me1_present, name = "H3K4me1 present")
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig3

2.2.1.4 H3K4me3

	in H3K4me3	absent H3K4me3
in svbF7 unbound	69	1862
absent svbF7 unbound	4738	46371

	in H3K4me3	absent H3K4me3
in svbF7 bound	109	192
absent svbF7 bound	4699	45486

pvalue_fixe = phyper(109 - 1, 301, 50486 - 301, 4808)
pvalue_nonfixe = phyper(69 - 1, 1931, 53040 - 1931, 4807)
presence = c(109,4699)
absence = c(192,45486)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me3_fixe)

presence = c(69,4738)
absence = c(1195,49770)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 2.268887910^{-22}

La pvalue du test de fisher pour les motifs fixé est de 2.306606710^{-36} Celle du test de fisher pour les motifs non fixé est de 2.425768410^{-5}`

total_svbF7_unbound_H3K4me3 = 69 + 1862
total_svbF7_bound_H3K4me3 = 109 + 192

percent_svbf7_H3K4me3_unbound = round(100 * 69 / total_svbF7_unbound_H3K4me3, 2 ) 
percent_svbf7_unbound_without_H3K4me3 = round(100 * 1862  / total_svbF7_unbound_H3K4me3, 2 ) 

percent_svbf7_H3K4me3_bound = round(100 * 109 / total_svbF7_bound_H3K4me3, 2 ) 
percent_svbf7_bound_without_H3K4me3 = round(100 * 192 / total_svbF7_bound_H3K4me3, 2 ) 

etat_svb = c("svbF7_bound","svb_unbound")
H3K4me3_absent = c(percent_svbf7_bound_without_H3K4me3,percent_svbf7_unbound_without_H3K4me3)

H3K4me3_present = c(percent_svbf7_H3K4me3_bound,percent_svbf7_H3K4me3_unbound)

data_H3K4me3 = data.frame(etat_svb,H3K4me3_absent,H3K4me3_present)
datatable(data_H3K4me3,rownames = F,filter = "none",colnames = c("Regions without H3K4me3","Regions with H3K4me3" ))

fig4 = plot_ly(data_H3K4me3, x = ~etat_svb,y = ~H3K4me3_absent, type = "bar",name = "H3K4me3 absent")
fig4 = fig4 %>% add_trace(y = ~H3K4me1_present, name = "H3K4me3 present")
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb")
fig4

fig4 = plot_ly(data_H3K4me3, x = ~etat_svb,y = ~H3K4me3_absent, type = "bar",name = "H3K4me3 absent")
fig4 = fig4 %>% add_trace(y = ~H3K4me3_present, name = "H3K4me3 present")
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig4

2.2.1.4.0.1 Rapport K4me1/ K4me3

rapp_fixe = round((contingence_H3K4me1_fixe[1,1]/total_K4me1_S2) / (contingence_H3K4me3_fixe[1,1]/total_K4me3_S2),2)
rapp_nonfixe = round((contingence_H3K4me1_nonfixe[1,1]/total_K4me1_S2) / (contingence_H3K4me3_nonfixe[1,1]/total_K4me3_S2),2)

Il y a 1.06 fois de H3K4me1 que de H3K4me3 fixé.

Il y a 5.84 fois de H3K4me1 que de H3K4me3 non fixé.

2.2.2 Sensible

2.2.2.1 H3K27ac

	in H3K27ac	absent H3K27ac
in svbF7 unbound	152	1738
absent svbF7 unbound	4996	24312

	in H3K27ac	absent H3K27ac
in svbF7 bound	190	152
absent svbF7 bound	4963	24886

pvalue_fixe = phyper(1190 - 1, 343, 30191 - 342, 5153)
pvalue_nonfixe = phyper(152 - 1, 1890, 31198 - 1890, 5148)
presence = c(190,4963)
absence = c(152,24886)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27ac_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27ac_fixe)

presence = c(152,4996)
absence = c(1738,24312)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27ac_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 4.446086610^{-29}

La pvalue du test de fisher pour les motifs fixé est de 4.135509110^{-59} Celle du test de fisher pour les motifs non fixé est de 2.014332510^{-28}`

total_svbF7_unbound_H3K27ac = 122 + 1738
total_svbF7_bound_H3K27ac = 190 + 152

percent_svbf7_H3K27ac_unbound = round(100 * 122 / total_svbF7_unbound_H3K27ac, 2 ) 
percent_svbf7_unbound_without_H3K27ac = round(100 * 1738 / total_svbF7_unbound_H3K27ac, 2 ) 

percent_svbf7_H3K27ac_bound = round(100 * 190 / total_svbF7_bound_H3K27ac, 2 ) 
percent_svbf7_bound_without_H3K27ac = round(100 * 157 / total_svbF7_bound_H3K27ac, 2 ) 



etat_svb = c("svbF7_bound","svb_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)

H3K27ac_present = c(percent_svbf7_H3K27ac_bound,percent_svbf7_H3K27ac_unbound)

data_H3K27ac = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

fig1 = plot_ly(data_H3K27ac, x = ~etat_svb,y = ~H3K27ac_absent, type = "bar",name = "H3K27ac absent")
fig1 = fig1 %>% add_trace(y = ~H3K27ac_present, name = "H3K27ac present")
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb")
fig1

fig1 = plot_ly(data_H3K27ac, x = ~etat_svb,y = ~H3K27ac_absent, type = "bar",name = "H3K27ac absent")
fig1 = fig1 %>% add_trace(y = ~H3K27ac_present, name = "H3K27ac present")
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig1

2.2.2.2 H3K27me3

	in H3K27me3	absent H3K27me3
in svbF7 unbound	728	1162
absent svbF7 unbound	12707	49806

	in H3K27me3	absent H3K27me3
in svbF7 bound	57	285
absent svbF7 bound	13381	46489

pvalue_fixe = phyper(57 - 1, 342, 60212 - 342, 13438)
pvalue_nonfixe = phyper(728 - 1, 1890, 64403 - 1890, 13435)
presence = c(57,13381)
absence = c(285,46489)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27me3_fixe)

presence = c(7282,12707)
absence = c(1162,49806)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 0.0039024 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 0.0110048 Celle du test de fisher pour les motifs non fixé est de 0`

total_svbF7_unbound_H3K27me3 = 728 + 1162
total_svbF7_bound_H3K27me3 = 57 + 285

percent_svbf7_H3K27me3_unbound = round(100 * 728 / total_svbF7_unbound_H3K27me3, 2 ) 
percent_svbf7_unbound_without_H3K27me3 = round(100 * 1162 / total_svbF7_unbound_H3K27me3, 2 ) 

percent_svbf7_H3K27me3_bound = round(100 * 57 / total_svbF7_bound_H3K27me3, 2 ) 
percent_svbf7_bound_without_H3K27me3 = round(100 * 285 / total_svbF7_bound_H3K27me3, 2 ) 


etat_svb = c("svbF7_bound","svb_unbound")
H3K27me3_absent = c(percent_svbf7_bound_without_H3K27me3,percent_svbf7_unbound_without_H3K27me3)

H3K27me3_present = c(percent_svbf7_H3K27me3_bound,percent_svbf7_H3K27me3_unbound)

data_H3K27me3 = data.frame(etat_svb,H3K27me3_absent,H3K27me3_present)
datatable(data_H3K27me3,rownames = F,filter = "none",colnames = c("Regions without H3K27me3","Regions with H3K27me3" ))

fig2 = plot_ly(data_H3K27me3, x = ~etat_svb,y = ~H3K27me3_absent, type = "bar",name = "H3K27me3 absent")
fig2 = fig2 %>% add_trace(y = ~H3K27me3_present, name = "H3K27me3 present")
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb")
fig2

fig2 = plot_ly(data_H3K27me3, x = ~etat_svb,y = ~H3K27me3_absent, type = "bar",name = "H3K27me3 absent")
fig2 = fig2 %>% add_trace(y = ~H3K27me3_present, name = "H3K27me3 present")
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig2

2.2.2.3 H3K4me1

	in H3K4me1	absent H3K4me1
in svbF7 unbound	302	1588
absent svbF7 unbound	8464	31826

	in H3K4me1	absent H3K4me1
in svbF7 bound	232	110
absent svbF7 bound	8540	31446

pvalue_fixe = phyper(232 - 1, 342, 40328 - 342, 8772)
pvalue_nonfixe = phyper(302 - 1, 1890, 42180 - 1890, 8766)
presence = c(232,8540)
absence = c(110,31446)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me1_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me1_fixe)

presence = c(302,8464)
absence = c(1588,31826)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me1_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me1_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 2.482721810^{-8}

La pvalue du test de fisher pour les motifs fixé est de 6.355519110^{-75} Celle du test de fisher pour les motifs non fixé est de 6.574769710^{-8}`

total_svbF7_unbound_H3K4me1 = 302 + 1588
total_svbF7_bound_H3K4me1 = 232 + 110

percent_svbf7_H3K4me1_unbound = round(100 * 321 / total_svbF7_unbound_H3K4me1, 2 ) 
percent_svbf7_unbound_without_H3K4me1 = round(100 * 1588  / total_svbF7_unbound_H3K4me1, 2 ) 

percent_svbf7_H3K4me1_bound = round(100 * 232 / total_svbF7_bound_H3K4me1, 2 ) 
percent_svbf7_bound_without_H3K4me1 = round(100 * 110 / total_svbF7_bound_H3K4me1, 2 ) 

etat_svb = c("svbF7_bound","svb_unbound")
H3K4me1_absent = c(percent_svbf7_bound_without_H3K4me1,percent_svbf7_unbound_without_H3K4me1)

H3K4me1_present = c(percent_svbf7_H3K4me1_bound,percent_svbf7_H3K4me1_unbound)

data_H3K4me1 = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1,rownames = F,filter = "none",colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig3 = plot_ly(data_H3K4me1, x = ~etat_svb,y = ~H3K4me1_absent, type = "bar",name = "H3K4me1 absent")
fig3 = fig3 %>% add_trace(y = ~H3K4me1_present, name = "H3K4me1 present")
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb")
fig3

fig3 = plot_ly(data_H3K4me1, x = ~etat_svb,y = ~H3K4me1_absent, type = "bar",name = "H3K4me1 absent")
fig3 = fig3 %>% add_trace(y = ~H3K4me1_present, name = "H3K4me1 present")
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig3

2.2.2.4 H3K4me3

	in H3K4me3	absent H3K4me3
in svbF7 unbound	60	1830
absent svbF7 unbound	4747	46405

	in H3K4me3	absent H3K4me3
in svbF7 bound	119	223
absent svbF7 bound	4689	45133

pvalue_fixe = phyper(119 - 1, 342, 50164 - 342, 4808)
pvalue_nonfixe = phyper(728 - 1, 1890, 64403 - 1890, 13435)
presence = c(57,13381)
absence = c(285,46489)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me3_fixe)

presence = c(7282,12707)
absence = c(1162,49806)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 0.0110048 Celle du test de fisher pour les motifs non fixé est de 0`

total_svbF7_unbound_H3K4me3 = 121 + 1206
total_svbF7_bound_H3K4me3 = 388 + 161

percent_svbf7_H3K4me3_unbound = round(100 * 121 / total_svbF7_unbound_H3K4me3, 2 ) 
percent_svbf7_unbound_without_H3K4me3 = round(100 * 1206  / total_svbF7_unbound_H3K4me3, 2 ) 

percent_svbf7_H3K4me3_bound = round(100 * 388 / total_svbF7_bound_H3K4me3, 2 ) 
percent_svbf7_bound_without_H3K4me3 = round(100 * 161 / total_svbF7_bound_H3K4me3, 2 ) 

etat_svb = c("svbF7_bound","svb_unbound")
H3K4me3_absent = c(percent_svbf7_bound_without_H3K4me3,percent_svbf7_unbound_without_H3K4me3)

H3K4me3_present = c(percent_svbf7_H3K4me3_bound,percent_svbf7_H3K4me3_unbound)

data_H3K4me3 = data.frame(etat_svb,H3K4me3_absent,H3K4me3_present)
datatable(data_H3K4me3,rownames = F,filter = "none",colnames = c("Regions without H3K4me3","Regions with H3K4me3" ))

fig4 = plot_ly(data_H3K4me3, x = ~etat_svb,y = ~H3K4me3_absent, type = "bar",name = "H3K4me3 absent")
fig4 = fig4 %>% add_trace(y = ~H3K4me1_present, name = "H3K4me3 present")
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb")
fig4

fig4 = plot_ly(data_H3K4me3, x = ~etat_svb,y = ~H3K4me3_absent, type = "bar",name = "H3K4me3 absent")
fig4 = fig4 %>% add_trace(y = ~H3K4me3_present, name = "H3K4me3 present")
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig4

2.2.2.4.0.1 Rapport K4me1/ K4me3

rapp_fixe = round((contingence_H3K4me1_fixe[1,1]/total_K4me1_S2) / (contingence_H3K4me3_fixe[1,1]/total_K4me3_S2),2)
rapp_nonfixe = round((contingence_H3K4me1_nonfixe[1,1]/total_K4me1_S2) / (contingence_H3K4me3_nonfixe[1,1]/total_K4me3_S2),2)

Il y a 2.23 fois de H3K4me1 que de H3K4me3 fixé.

Il y a 0.02 fois de H3K4me1 que de H3K4me3 non fixé.

2.2.3 Embryon

2.2.3.1 8-12h

2.2.3.1.1 H3K27ac

	in H3K27ac	absent H3K27ac
in svbF7 unbound	196	1670
absent svbF7 unbound	9935	107649

	in H3K27ac	absent H3K27ac
in svbF7 bound	65	303
absent svbF7 bound	10067	98675

pvalue_fixe = phyper(65 - 1, 368, 109110 - 368, 10132)
pvalue_nonfixe = phyper(196 - 1, 1866, 119450 - 1866, 10131)
presence = c(65,10067)
absence = c(303,98675)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27ac_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27ac_fixe)

presence = c(196,9935)
absence = c(1670,107649)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H327ac_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H327ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 0.9999996 Celle du test de hypergéométrique pour les motifs non fixé est de 0.9987769

La pvalue du test de fisher pour les motifs fixé est de 5.111577810^{-7} Celle du test de fisher pour les motifs non fixé est de 0.0022066`

total_svbF7_unbound_H3K27ac = 196 + 1670
total_svbF7_bound_H3K27ac = 65 + 303

percent_svbf7_H3K27ac_unbound = round(100 * 196 / total_svbF7_unbound_H3K27ac, 2 ) 
percent_svbf7_unbound_without_H3K27ac = round(100 * 1670 / total_svbF7_unbound_H3K27ac, 2 ) 

percent_svbf7_H3K27ac_bound = round(100 * 65 / total_svbF7_bound_H3K27ac, 2 ) 
percent_svbf7_bound_without_H3K27ac = round(100 * 303 / total_svbF7_bound_H3K27ac, 2 ) 



etat_svb = c("svbF7_bound","svb_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)

H3K27ac_present = c(percent_svbf7_H3K27ac_bound,percent_svbf7_H3K27ac_unbound)

data_H3K27ac = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

fig1 = plot_ly(data_H3K27ac, x = ~etat_svb,y = ~H3K27ac_absent, type = "bar",name = "H3K27ac absent")
fig1 = fig1 %>% add_trace(y = ~H3K27ac_present, name = "H3K27ac present")
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb")
fig1

fig1 = plot_ly(data_H3K27ac, x = ~etat_svb,y = ~H3K27ac_absent, type = "bar",name = "H3K27ac absent")
fig1 = fig1 %>% add_trace(y = ~H3K27ac_present, name = "H3K27ac present")
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig1

2.2.3.1.2 H3K27me3

	in H3K27me3	absent H3K27me3
in svbF7 unbound	255	1611
absent svbF7 unbound	6507	80721

	in H3K27me3	absent H3K27me3
in svbF7 bound	38	330
absent svbF7 bound	6724	76125

pvalue_fixe = phyper(38 - 1, 368, 83217 - 368 , 6762)
pvalue_nonfixe = phyper(255 - 1, 1866, 89094 - 1866, 6762)
presence = c(30,6724)
absence = c(330,76125)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27me3_fixe)

presence = c(255,6507)
absence = c(1611,80721)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 0.9232816 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 0.8466164 Celle du test de fisher pour les motifs non fixé est de 8.067370810^{-20}`

total_svbF7_unbound_H3K27me3 = 255 + 1611
total_svbF7_bound_H3K27me3 = 38 + 330

percent_svbf7_H3K27me3_unbound = round(100 * 255 / total_svbF7_unbound_H3K27me3, 2 ) 
percent_svbf7_unbound_without_H3K27me3 = round(100 * 1611/ total_svbF7_unbound_H3K27me3, 2 ) 

percent_svbf7_H3K27me3_bound = round(100 * 38 / total_svbF7_bound_H3K27me3, 2 ) 
percent_svbf7_bound_without_H3K27me3 = round(100 * 330 / total_svbF7_bound_H3K27me3, 2 ) 


etat_svb = c("svbF7_bound","svb_unbound")
H3K27me3_absent = c(percent_svbf7_bound_without_H3K27me3,percent_svbf7_unbound_without_H3K27me3)

H3K27me3_present = c(percent_svbf7_H3K27me3_bound,percent_svbf7_H3K27me3_unbound)

data_H3K27me3 = data.frame(etat_svb,H3K27me3_absent,H3K27me3_present)
datatable(data_H3K27me3,rownames = F,filter = "none",colnames = c("Regions without H3K27me3","Regions with H3K27me3" ))

fig2 = plot_ly(data_H3K27me3, x = ~etat_svb,y = ~H3K27me3_absent, type = "bar",name = "H3K27me3 absent")
fig2 = fig2 %>% add_trace(y = ~H3K27me3_present, name = "H3K27me3 present")
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb")
fig2

fig2 = plot_ly(data_H3K27me3, x = ~etat_svb,y = ~H3K27me3_absent, type = "bar",name = "H3K27me3 absent")
fig2 = fig2 %>% add_trace(y = ~H3K27me3_present, name = "H3K27me3 present")
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig2

2.2.3.1.3 H3K4me1

	in H3K4me1	absent H3K4me1
in svbF7 unbound	336	1530
absent svbF7 unbound	14247	105586

	in H3K4me1	absent H3K4me1
in svbF7 bound	104	264
absent svbF7 bound	14483	96147

pvalue_fixe = phyper(336 - 1, 368, 121699 - 368, 14583)
pvalue_nonfixe = phyper(104 - 1, 1866, 110998 - 1866, 14587)
presence = c(104,14483)
absence = c(264,96147)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me1_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me1_fixe)

presence = c(336,14247)
absence = c(1530,105586)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me1_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me1_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 7.334647710^{-28}

La pvalue du test de fisher pour les motifs fixé est de 2.453873510^{-14} Celle du test de fisher pour les motifs non fixé est de 2.628965710^{-14}`

total_svbF7_unbound_H3K4me1 = 336 + 1530
total_svbF7_bound_H3K4me1 = 104 + 264

percent_svbf7_H3K4me1_unbound = round(100 * 336 / total_svbF7_unbound_H3K4me1, 2 ) 
percent_svbf7_unbound_without_H3K4me1 = round(100 * 1530 / total_svbF7_unbound_H3K4me1, 2 ) 

percent_svbf7_H3K4me1_bound = round(100 * 104 / total_svbF7_bound_H3K4me1, 2 ) 
percent_svbf7_bound_without_H3K4me1 = round(100 * 264 / total_svbF7_bound_H3K4me1, 2 ) 

etat_svb = c("svbF7_bound","svb_unbound")
H3K4me1_absent = c(percent_svbf7_bound_without_H3K4me1,percent_svbf7_unbound_without_H3K4me1)

H3K4me1_present = c(percent_svbf7_H3K4me1_bound,percent_svbf7_H3K4me1_unbound)

data_H3K4me1 = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1,rownames = F,filter = "none",colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig3 = plot_ly(data_H3K4me1, x = ~etat_svb,y = ~H3K4me1_absent, type = "bar",name = "H3K4me1 absent")
fig3 = fig3 %>% add_trace(y = ~H3K4me1_present, name = "H3K4me1 present")
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb")
fig3

fig3 = plot_ly(data_H3K4me1, x = ~etat_svb,y = ~H3K4me1_absent, type = "bar",name = "H3K4me1 absent")
fig3 = fig3 %>% add_trace(y = ~H3K4me1_present, name = "H3K4me1 present")
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig3

2.2.3.1.4 H3K4me3

	in H3K4me3	absent H3K4me3
in svbF7 unbound	114	1752
absent svbF7 unbound	6058	78680

	in H3K4me3	absent H3K4me3
in svbF7 bound	48	320
absent svbF7 bound	6124	74548

pvalue_fixe = phyper(48 - 1, 368, 81040 - 368, 6172)
pvalue_nonfixe = phyper(114 - 1, 1866, 866004 - 1866, 6172)
presence = c(48,6124)
absence = c(320,74548)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me3_fixe)

presence = c(114,6058)
absence = c(1752,78680)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 0.999802 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 3.404928210^{-4} Celle du test de fisher pour les motifs non fixé est de 0.0921426`

total_svbF7_unbound_H3K4me3 = 114 + 1752
total_svbF7_bound_H3K4me3 = 48 + 320

percent_svbf7_H3K4me3_unbound = round(100 * 114 / total_svbF7_unbound_H3K4me3, 2 ) 
percent_svbf7_unbound_without_H3K4me3 = round(100 * 1752  / total_svbF7_unbound_H3K4me3, 2 ) 

percent_svbf7_H3K4me3_bound = round(100 * 48 / total_svbF7_bound_H3K4me3, 2 ) 
percent_svbf7_bound_without_H3K4me3 = round(100 * 320 / total_svbF7_bound_H3K4me3, 2 ) 

etat_svb = c("svbF7_bound","svb_unbound")
H3K4me3_absent = c(percent_svbf7_bound_without_H3K4me3,percent_svbf7_unbound_without_H3K4me3)

H3K4me3_present = c(percent_svbf7_H3K4me3_bound,percent_svbf7_H3K4me3_unbound)

data_H3K4me3 = data.frame(etat_svb,H3K4me3_absent,H3K4me3_present)
datatable(data_H3K4me3,rownames = F,filter = "none",colnames = c("Regions without H3K4me3","Regions with H3K4me3" ))

fig4 = plot_ly(data_H3K4me3, x = ~etat_svb,y = ~H3K4me3_absent, type = "bar",name = "H3K4me3 absent")
fig4 = fig4 %>% add_trace(y = ~H3K4me1_present, name = "H3K4me3 present")
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb")
fig4

fig4 = plot_ly(data_H3K4me3, x = ~etat_svb,y = ~H3K4me3_absent, type = "bar",name = "H3K4me3 absent")
fig4 = fig4 %>% add_trace(y = ~H3K4me3_present, name = "H3K4me3 present")
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig4

2.2.3.1.5 Rapport K4me1/ K4me3

rapp_fixe = round((contingence_H3K4me1_fixe[1,1]/total_K4me1_8_12) / (contingence_H3K4me3_fixe[1,1]/total_K4me3_8_12),2)
rapp_nonfixe = round((contingence_H3K4me1_nonfixe[1,1]/total_K4me1_8_12) / (contingence_H3K4me3_nonfixe[1,1]/total_K4me3_8_12),2)

Il y a 0.92 fois de H3K4me1 que de H3K4me3 fixé.

Il y a 1.25 fois de H3K4me1 que de H3K4me3 non fixé.

2.2.3.2 12-16h

2.2.3.2.1 H3K27ac

	in H3K27ac	absent H3K27ac
in svbF7 unbound	184	1682
absent svbF7 unbound	10275	120495

	in H3K27ac	absent H3K27ac
in svbF7 bound	88	280
absent svbF7 bound	10371	109254

pvalue_fixe = phyper(88 - 1, 368, 119993 - 368, 10459)
pvalue_nonfixe = phyper(184 - 1, 1866, 132606 - 1866, 10459)
presence = c(88,10371)
absence = c(280,109254)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27ac_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27ac_fixe)

presence = c(184,10275)
absence = c(1682,120495)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27ac_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27ac_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 0.9988463

La pvalue du test de fisher pour les motifs fixé est de 2.205439110^{-18} Celle du test de fisher pour les motifs non fixé est de 0.0021011`

total_svbF7_unbound_H3K27ac = 184 + 1682
total_svbF7_bound_H3K27ac = 88 + 280

percent_svbf7_H3K27ac_unbound = round(100 * 184 / total_svbF7_unbound_H3K27ac, 2 ) 
percent_svbf7_unbound_without_H3K27ac = round(100 * 1682 / total_svbF7_unbound_H3K27ac, 2 ) 

percent_svbf7_H3K27ac_bound = round(100 * 88 / total_svbF7_bound_H3K27ac, 2 ) 
percent_svbf7_bound_without_H3K27ac = round(100 * 280 / total_svbF7_bound_H3K27ac, 2 ) 



etat_svb = c("svbF7_bound","svb_unbound")
H3K27ac_absent = c(percent_svbf7_bound_without_H3K27ac,percent_svbf7_unbound_without_H3K27ac)

H3K27ac_present = c(percent_svbf7_H3K27ac_bound,percent_svbf7_H3K27ac_unbound)

data_H3K27ac = data.frame(etat_svb,H3K27ac_absent,H3K27ac_present)
datatable(data_H3K27ac,rownames = F,filter = "none",colnames = c("Regions without H3K27ac","Regions with H3K27ac" ))

fig1 = plot_ly(data_H3K27ac, x = ~etat_svb,y = ~H3K27ac_absent, type = "bar",name = "H3K27ac absent")
fig1 = fig1 %>% add_trace(y = ~H3K27ac_present, name = "H3K27ac present")
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb")
fig1

fig1 = plot_ly(data_H3K27ac, x = ~etat_svb,y = ~H3K27ac_absent, type = "bar",name = "H3K27ac absent")
fig1 = fig1 %>% add_trace(y = ~H3K27ac_present, name = "H3K27ac present")
fig1 = fig1 %>% layout(yaxis = list(title = "percent"))
fig1 = fig1 %>% layout(title = "H3K27ac enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig1

2.2.3.2.2 H3K27me3

	in H3K27me3	absent H3K27me3
in svbF7 unbound	261	1605
absent svbF7 unbound	6345	76742

	in H3K27me3	absent H3K27me3
in svbF7 bound	40	328
absent svbF7 bound	6566	72659

pvalue_fixe = phyper(40 - 1, 368, 79593 - 368, 6606)
pvalue_nonfixe = phyper(261 - 1, 1866, 84953 - 1866, 6606)
presence = c(40,6566)
absence = c(328,72659)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K27me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K27me3_fixe)

presence = c(261,6345)
absence = c(1605,76742)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K27me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K27me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 0.950963 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 0.0871654 Celle du test de fisher pour les motifs non fixé est de 2.617652810^{-20}`

total_svbF7_unbound_H3K27me3 = 261 + 1605
total_svbF7_bound_H3K27me3 = 40 + 328

percent_svbf7_H3K27me3_unbound = round(100 * 261 / total_svbF7_unbound_H3K27me3, 2 ) 
percent_svbf7_unbound_without_H3K27me3 = round(100 * 1605 / total_svbF7_unbound_H3K27me3, 2 ) 

percent_svbf7_H3K27me3_bound = round(100 * 40 / total_svbF7_bound_H3K27me3, 2 ) 
percent_svbf7_bound_without_H3K27me3 = round(100 * 328 / total_svbF7_bound_H3K27me3, 2 ) 


etat_svb = c("svbF7_bound","svb_unbound")
H3K27me3_absent = c(percent_svbf7_bound_without_H3K27me3,percent_svbf7_unbound_without_H3K27me3)

H3K27me3_present = c(percent_svbf7_H3K27me3_bound,percent_svbf7_H3K27me3_unbound)

data_H3K27me3 = data.frame(etat_svb,H3K27me3_absent,H3K27me3_present)
datatable(data_H3K27me3,rownames = F,filter = "none",colnames = c("Regions without H3K27me3","Regions with H3K27me3" ))

fig2 = plot_ly(data_H3K27me3, x = ~etat_svb,y = ~H3K27me3_absent, type = "bar",name = "H3K27me3 absent")
fig2 = fig2 %>% add_trace(y = ~H3K27me3_present, name = "H3K27me3 present")
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb")
fig2

fig2 = plot_ly(data_H3K27me3, x = ~etat_svb,y = ~H3K27me3_absent, type = "bar",name = "H3K27me3 absent")
fig2 = fig2 %>% add_trace(y = ~H3K27me3_present, name = "H3K27me3 present")
fig2 = fig2 %>% layout(yaxis = list(title = "percent"))
fig2 = fig2 %>% layout(title = "H3K27me3 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig2

2.2.3.2.3 H3K4me1

	in H3K4me1	absent H3K4me1
in svbF7 unbound	263	1603
absent svbF7 unbound	12340	118669

	in H3K4me1	absent H3K4me1
in svbF7 bound	81	287
absent svbF7 bound	12562	107325

pvalue_fixe = phyper(81 - 1, 368, 120218 - 368, 12606)
pvalue_nonfixe = phyper(263 - 1, 1866, 132875 - 1866, 12603)
presence = c(263,12340)
absence = c(1603,118669)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me1_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me1_fixe)

presence = c(81,12562)
absence = c(287,107325)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me1_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me1_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 1

La pvalue du test de fisher pour les motifs fixé est de 1.235597210^{-10} Celle du test de fisher pour les motifs non fixé est de 1.269695310^{-10}`

total_svbF7_unbound_H3K4me1 = 263 + 1603
total_svbF7_bound_H3K4me1 = 81 + 287

percent_svbf7_H3K4me1_unbound = round(100 * 263 / total_svbF7_unbound_H3K4me1, 2 ) 
percent_svbf7_unbound_without_H3K4me1 = round(100 * 1603 / total_svbF7_unbound_H3K4me1, 2 ) 

percent_svbf7_H3K4me1_bound = round(100 * 81 / total_svbF7_bound_H3K4me1, 2 ) 
percent_svbf7_bound_without_H3K4me1 = round(100 * 287 / total_svbF7_bound_H3K4me1, 2 ) 

etat_svb = c("svbF7_bound","svb_unbound")
H3K4me1_absent = c(percent_svbf7_bound_without_H3K4me1,percent_svbf7_unbound_without_H3K4me1)

H3K4me1_present = c(percent_svbf7_H3K4me1_bound,percent_svbf7_H3K4me1_unbound)

data_H3K4me1 = data.frame(etat_svb,H3K4me1_absent,H3K4me1_present)
datatable(data_H3K4me1,rownames = F,filter = "none",colnames = c("Regions without H3K4me1","Regions with H3K4me1" ))

fig3 = plot_ly(data_H3K4me1, x = ~etat_svb,y = ~H3K4me1_absent, type = "bar",name = "H3K4me1 absent")
fig3 = fig3 %>% add_trace(y = ~H3K4me1_present, name = "H3K4me1 present")
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb")
fig3

fig3 = plot_ly(data_H3K4me1, x = ~etat_svb,y = ~H3K4me1_absent, type = "bar",name = "H3K4me1 absent")
fig3 = fig3 %>% add_trace(y = ~H3K4me1_present, name = "H3K4me1 present")
fig3 = fig3 %>% layout(yaxis = list(title = "percent"))
fig3 = fig3 %>% layout(title = "H3K4me1 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig3

2.2.3.2.4 H3K4me3

	in H3K4me3	absent H3K4me3
in svbF7 unbound	120	1746
absent svbF7 unbound	6646	92671

	in H3K4me3	absent H3K4me3
in svbF7 bound	62	306
absent svbF7 bound	6705	86603

pvalue_fixe = phyper(62 - 1, 368, 93676 - 368, 6767)
pvalue_nonfixe = phyper(120 - 1, 1866, 101183 - 1866, 6766)
presence = c(120,6646)
absence = c(1746,92671)
svbF7 = c("in svbF7 bound", "absent svbF7 bound")
contingence_H3K4me3_fixe = data.frame(presence,absence, row.names = svbF7)
fisher = fisher.test(contingence_H3K4me3_fixe)

presence = c(120,6646)
absence = c(1746,92671)
svbF7 = c("in svbF7 unbound", "absent svbF7 unbound")
contingence_H3K4me3_nonfixe = data.frame(presence,absence, row.names = svbF7)
fisher_nonfixe = fisher.test(contingence_H3K4me3_nonfixe)

La pvalue du test de hypergéométrique pour les motifs fixé est de 1 Celle du test de hypergéométrique pour les motifs non fixé est de 0.3142804

La pvalue du test de fisher pour les motifs fixé est de 0.7081678 Celle du test de fisher pour les motifs non fixé est de 0.7081678`

total_svbF7_unbound_H3K4me3 = 120 + 1746
total_svbF7_bound_H3K4me3 = 62 + 306

percent_svbf7_H3K4me3_unbound = round(100 * 120 / total_svbF7_unbound_H3K4me3, 2 ) 
percent_svbf7_unbound_without_H3K4me3 = round(100 * 1746  / total_svbF7_unbound_H3K4me3, 2 ) 

percent_svbf7_H3K4me3_bound = round(100 * 62 / total_svbF7_bound_H3K4me3, 2 ) 
percent_svbf7_bound_without_H3K4me3 = round(100 * 306 / total_svbF7_bound_H3K4me3, 2 ) 

etat_svb = c("svbF7_bound","svb_unbound")
H3K4me3_absent = c(percent_svbf7_bound_without_H3K4me3,percent_svbf7_unbound_without_H3K4me3)

H3K4me3_present = c(percent_svbf7_H3K4me3_bound,percent_svbf7_H3K4me3_unbound)

data_H3K4me3 = data.frame(etat_svb,H3K4me3_absent,H3K4me3_present)
datatable(data_H3K4me3,rownames = F,filter = "none",colnames = c("Regions without H3K4me3","Regions with H3K4me3" ))

fig4 = plot_ly(data_H3K4me3, x = ~etat_svb,y = ~H3K4me3_absent, type = "bar",name = "H3K4me3 absent")
fig4 = fig4 %>% add_trace(y = ~H3K4me1_present, name = "H3K4me3 present")
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb")
fig4

fig4 = plot_ly(data_H3K4me3, x = ~etat_svb,y = ~H3K4me3_absent, type = "bar",name = "H3K4me3 absent")
fig4 = fig4 %>% add_trace(y = ~H3K4me3_present, name = "H3K4me3 present")
fig4 = fig4 %>% layout(yaxis = list(title = "percent"))
fig4 = fig4 %>% layout(title = "H3K4me3 enrichment on svbF7 bound or not by Svb", barmode = "stack")
fig4

2.2.3.2.5 Rapport K4me1/ K4me3

rapp_fixe = round((contingence_H3K4me1_fixe[1,1]/total_K4me1_12_16) / (contingence_H3K4me3_fixe[1,1]/total_K4me3_12_16),2)
rapp_nonfixe = round((contingence_H3K4me1_nonfixe[1,1]/total_K4me1_12_16) / (contingence_H3K4me3_nonfixe[1,1]/total_K4me3_12_16),2)

Il y a 1.18 fois de H3K4me1 que de H3K4me3 fixé.

Il y a 0.36 fois de H3K4me1 que de H3K4me3 non fixé.

3 Conclusion

Lorsque l’on compare les motifs de fixation de Shavenbaby (svbF7 ou ovoQ6) et ses pics de fixation ( embryon, S2 ( sensible ou stringent)) nous observons que 70% à 85% des pics présentent des motifs svbF7 et/ou ovoQ6. Nous observons ensuite que ces motifs présentent des enrichissements en histones différents s’ils sont fixés ou non par Shavenbaby. Dans les cellules S2, les motifs svbF7 fixés par le facteur de transcription présentent un enrichissement en histone H3K27ac, H3K4me1 et H3K4me3. Les motifs fixés présentent un enrichissement en H3K4me1 plus fort qu’en H3K4me3, ce qui laisse à penser que Shavenbaby se fixe sur des régions enhancer préférentiellement. On observe que les motifs svbF7 non fixés en cellules S2 quant à eux présentent un enrichissement moindre en ces marques d’histones voire une absence totale. Ces motifs non fixés présentent un enrichissement en H3K27me3 alors que les motifs fixés n’en présentent pas ou peu. Ces observations sont valables que le motif soit agrandi de 3kb ou 300pb ( taille moyenne des pics). Dans les embryons (8-12h ou 12-16h), les motifs svbF7 fixés par Shavenbaby présentent un enrichissement en H3K4me1 et H3K27ac. Les motifs svbF7 présentent autant d’H3K27me3 ou de H3K4me3 qu’ils soient fixés ou non. En conclusion, les motifs svbF7 fixés présentent un enrichissement en H3K4me1 et H3K27ac qu’on observe en S2 ou en embryon.